ТРАНСПОРТНОЕ СРЕДСТВО И СПОСОБ УПРАВЛЕНИЯ СВЕТОПРОНИЦАЕМОСТЬЮ ОКОН ТРАНСПОРТНОГО СРЕДСТВА

Предложено транспортное средство, содержащее камеру, окно, светочувствительный датчик, контроллер, искусственную нейронную сеть. Камера выдает данные изображения, соответствующие одному или более изображениям, снятым с занимающего место человека в транспортном средстве. Датчик распознает поток излучения, переданный через окно, имеющее переменную светопроницаемость, к занимающему место человеку. Искусственная нейронная сеть классифицирует восприятие переданного потока излучения занимающим место человеком на основе изображений и переданного потока излучения. С помощью нейронной сети формируют показатель сходства для каждого из множества классов конфигурации занимающего место человека, с использованием данных изображения в качестве входных данных, при этом каждый из множества классов связан с соответствующим восприятием потока излучения занимающим место человеком. Выбирают класс конфигурации занимающего место человека, связанный с занимающим место человеком, основываясь на показателе сходства ...

Вычислительное устройство выявления неисправностей напольного технологического оборудования

Полезная модель относится к области технической кибернетики. Вычислительное устройство выявления неисправностей напольного технологического оборудования содержит блок памяти, два микропроцессора, источник питания, нейропроцессор, блок увязки с системой технической диагностики и мониторинга, коммутатор, причем блок увязки с системой технической диагностики и мониторинга подключен к этой системе интерфейсным соединением, блок увязки с системой технической диагностики и мониторинга соединен с первым микропроцессором, блок увязки с системой технической диагностики и мониторинга подключен к источнику питания, к которому подведено общее напряжение питания, первый микропроцессор, в свою очередь, связан с коммутатором, который содержит четыре вывода, прямое и обратное соединение с ISA - шиной, a ISA - шина также имеет соединение в прямом и обратном направлении с персональным компьютером, общее напряжение питания подключено к первому микропроцессору и коммутатору, один из выводов коммутатора подключен ...

СПОСОБ, УСТРОЙСТВО И СЕРВЕР ДЛЯ ОПРЕДЕЛЕНИЯ ПЛАНА СЪЕМКИ ИЗОБРАЖЕНИЯ

Изобретение относится к определению плана съемки изображения. Техническим результатом является повышение точности классификации изображений. В способе получают галерею пользовательского терминала; осуществляют идентификацию и маркировку изображения; получают обучающий набор выборки; вводят каждую из множества последовательностей обучающих изображений; обучают коэффициенты признака между уровнями скрытых узлов модели определения исходного изображения плана съемки; получают тестовый набор выборки; осуществляют идентификацию тестовых изображений; определяют точность классификации модели определения плана съемки изображения; если точность классификации меньше заданного порогового значения, выполняют: обновление обучающего набора выборки; обучение, в соответствии с обновленным обучающим набором выборки, коэффициентов признака между соответствующими уровнями скрытых узлов модели определения плана съемки изображения; выполнения итерации обновления модели определения плана съемки изображения; выполнение ...

УСТРОЙСТВО И СПОСОБ ДЛЯ БИОМЕТРИЧЕСКОЙ ИДЕНТИФИКАЦИИ ПОЛЬЗОВАТЕЛЯ С ИСПОЛЬЗОВАНИЕМ РЧ (РАДИОЧАСТОТНОГО) РАДАРА

Изобретение относится к области идентификации пользователя. Технические результаты заключаются в обеспечении непрерывной идентификации пользователя без запроса у пользователя данных для идентификации, сложности подделки биометрических данных пользователя, возможности встраивания в носимые устройства, отсутствия необходимости непосредственного контакта с кожей пользователя. Такие результаты достигаются за счет того, что устройство содержит передающую антенну; приемную антенну; передатчик для генерации сверхширокополосных сигналов и испускания сверхширокополосных сигналов в ткани части тела пользователя через передающую антенну; приемник для приема сигналов, прошедших через ткани части тела пользователя, через приемную антенну; аналого-цифровой преобразователь для преобразования принятых сигналов в цифровые сигналы; память для хранения параметров обученного средства классификации; и центральный процессор для анализа цифровых сигналов посредством обученного средства классификации с использованием ...

СПОСОБ РАСШИФРОВКИ ДЕФЕКТОГРАММ И ОЦИФРОВАННЫХ СИГНАЛОВ ИССЛЕДОВАНИЯ ТВЁРДЫХ ТЕЛ

Использование: для расшифровки дефектограмм и оцифрованных сигналов исследования твердых тел. Сущность изобретения заключается в том, что расшифровка осуществляется с помощью предобученной на размеченных данных об амплитудно-частотных и спектральных характеристиках нейронной сети и позволяет получить проверенный методом кросс-валидации машиночитаемый или человекочитаемый ответ о наличии или отсутствии исследуемых характеристик твердого тела или управляющие команды, связанные с наличием или отсутствием исследуемых характеристик. Технический результат: обеспечение возможности упрощения и повышения качества расшифровки дефектограмм и оцифрованных сигналов исследования твердых тел. 1 ил.

СПОСОБ ИДЕНТИФИКАЦИИ ЧЕЛОВЕКА В РЕЖИМЕ ОДНОВРЕМЕННОЙ РАБОТЫ ГРУППЫ ВИДЕОКАМЕР

Изобретение относится к обработке изображений. Технический результат – повышение точности идентификации лиц людей в системах видеонаблюдения, использующих множество камер. Компьютерно-реализуемый способ идентификации человека в режиме одновременной работы группы видеокамер, выполняемый с помощью вычислительного устройства, содержит этапы, на которых получают изображения лица человека от группы видеокамер, причем каждая из камер фиксирует изображение человека под определенным ракурсом, преобразовывают полученные от видеокамер изображения в векторную форму, идентифицируют человека и выявляют соответствующую ему информацию об имеющихся векторных изображениях в базе данных для различных ракурсов, полученных от группы видеокамер, сравнивают полученные от видеокамер векторные изображения с векторными изображениями человека, хранящимися в базе данных, определяют для каждой видеокамеры из группы целевой вектор изображения для каждой видеокамеры на основании векторной близости, присваивают целевой ...

Способ опознавания личности по рисунку вен ладони

Изобретение относится к биометрии, а именно к технике защиты различных объектов от доступа посторонних лиц путем идентификации личности по рисунку вен ладони (РВЛ). Способ опознавания личности по рисунку вен ладони, включающий потоковое считывание рисунка вен ладони субъекта идентификации, получение из потока кадра, анализ полученного кадра, факт биометрической идентификации, при этом перед потоковым считыванием рисунка вен ладони субъекта идентификации проводят выбор целевого субъекта идентификации и соответствующей ему нейросети, а после получения из потока кадра, проводят его предварительную обработку, не изменяющую тип данных, при этом анализ полученного кадра осуществляют сверточной нейронной сетью, предварительно обученной для бинарной классификации изображений известного формата на два класса, один из которых указывает на соответствие кадра с выбранным субъектом идентификации, а второй - на несоотвествие кадра с выбранным субъектом идентификации, тогда как результатом анализа является ...

Способ показа объектов в последовательности изображений, полученных от стационарной видеокамеры

Изобретение относится к области видеонаблюдения, а именно к технологиям обнаружения объектов в последовательности изображений, полученных от стационарной видеокамеры. Техническим результатом является повышение качества синтетических кадров за счет детектирования единого объекта, используя алгоритм отслеживания движения объекта. Предложен способ показа объектов в последовательности изображений, полученных от стационарной видеокамеры. Согласно способу, в исходной последовательности кадров обнаруживают объекты, для каждого из обнаруженных объектов строят траекторию его движения. Далее формируют очередь траекторий движения объектов, составляют расписание показа объектов, по которому строят план формирования синтетических кадров, формируют очередной синтетический кадр, показывают оператору на экране сформированные синтетические кадры. Перед формированием очереди траекторий движения объектов осуществляют склеивание траекторий движения осколков в единую траекторию движения.

ЭЛЕКТРОННОЕ УСТРОЙСТВО И СПОСОБ УПРАВЛЕНИЯ ИМ

ТРАНСПОРТНОЕ СРЕДСТВО И СПОСОБ УПРАВЛЕНИЯ СВЕТОПРОНИЦАЕМОСТЬЮ ОКОН ТРАНСПОРТНОГО СРЕДСТВА

СОЗДАНИЕ ГИБКИХ СТРУКТУРНЫХ ОПИСАНИЙ ДЛЯ ДОКУМЕНТОВ С ПОВТОРЯЮЩИМИСЯ НЕРЕГУЛЯРНЫМИ СТРУКТУРАМИ

... 1. Метод создания гибкого структурного описания, содержащий:получение изображения документа определенного типа, содержащего таблицу;получение позиции описывающей запись в таблице;поиск элементов заголовка на основе позиции;обнаружение полей данных и опорных элементов для позиции таблицы;создание, используя процессор, гибкого структурного описания для документа определенного типа, включающего набор элементов поиска для каждого из полей данных на изображении документа, а также элементов заголовка;наложение гибкого структурного описания на изображение иизвлечение данных из изображения в соответствии с параметрами гибкого структурного описания наложением гибкого структурного описания на изображение.2. Метод по п. 1, дополнительно включающий корректировку, используя процессор, гибкого структурного описания на основе пользовательских исправлений обнаруженных полей данных, элементов заголовка и/или опорных элементов.3. Метод по п. 1, в котором таблица занимает несколько страниц документа и в котором ...

AUSWAHL VON AUSGEWOGENEN SONDENSTELLEN FÜR 3-D AUSRICHTUNGSALGORITHMEN

Techniken umfassen Systeme, computergestützte Verfahren und computerlesbare Medien zur Auswahl der Platzierung von dreidimensionalen (3D) Sonden, die zur Auswertung einer 3D-Ausrichtungspose eines Laufzeit-3D-Bildes in einem 3D-Ausrichtungssystem zur Abschätzung der Pose eines trainierten 3D-Modellbildes in jenem 3D-Laufzeit-Bild verwendet werden. Es wird eine Vielzahl von Merkmalen erzeugt, die mit einer ersten Vielzahl von Punkten von Interesse aus einem 3D-Bild assoziiert sind, wobei jedes Merkmal Daten enthält, die die 3D-Eigenschaften eines assoziierten Punktes aus der Vielzahl von Punkten von Interesse angeben. Eine zweite Vielzahl von Punkten von Interesse wird aus der ersten Vielzahl von Punkten von Interesse ausgewählt, basierend zumindest teilweise auf der Vielzahl von Merkmalen, die mit der ersten Vielzahl von Punkten von Interesse assoziiert sind. Platzierungen einer Vielzahl von 3D-Sonden werden basierend zumindest teilweise auf der zweiten Vielzahl von Punkten von Interesse ...

FAHRZEUGBETRIEBSKENNZEICHNUNG

Die Offenbarung stellt eine Fahrzeugbetriebskennzeichnung bereit. Ein Computer, der einen Prozessor und einen Speicher beinhaltet, wobei der Speicher Anweisungen beinhaltet, die von dem Prozessor auszuführen sind, um Muster in ersten Szenarien mit hoher Erwartung auf Grundlage der Benutzeridentifikation zu identifizieren, wobei Szenarien mit hoher Erwartung Videosequenzen beinhalten, wobei sich ein erstes Fahrzeug innerhalb einer spezifizierten Entfernung von einem ersten Objekt in einer ersten Umgebung um das erste Fahrzeug befindet, wobei die Benutzeridentifikation durch Betrachten von Abschnitten einer entsprechenden Videosequenz bestimmt wird. Ein erstes Modell, das ein erstes tiefes neuronales Netzwerk beinhaltet, kann trainiert werden, um zweite Szenarien mit hoher Erwartung auf Grundlage der in den ersten Szenarien mit hoher Erwartung identifizierten Muster zu bestimmen, und ein zweites Modell, das ein zweites tiefes neuronales Netzwerk beinhaltet, kann trainiert werden, um Standorte ...

VERFAHREN UND VORRICHTUNG ZUR AUTOMATISCHEN ZEICHENERKENNUNG

Pattern regeneration device

A periodic or chaotic output is generated by a pattern generation unit (2) on the basis of a relationship between an entered pattern for regeneration and a stored pattern, which is stored in a memory (1). The generated pattern is converted by a conversion unit (4) of the regeneration device into the next stored pattern. Searching the stored patterns is controlled according to the behaviour of the output, and the search range can be set dynamically. ...

Joint Depth estimation and semantic labeling of a single image

A method, an apparatus, a computer program product for augmented reality

The invention relates to a method; an apparatus and a computer program product. The method comprises receiving video frames in electronic form: segmenting a video frame to detect interesting parts 520 such as eyes, mouth or lips: processing the interesting parts, e.g. enlarge them, to obtain altered parts 530; generating an overlay 540 comprising data on the altered parts: and applying the overlay 540 to the video frame to produce a presentation of the video frames including altered parts. The embodiments can be utilized as an augmented reality conversation aid to improve lip-reading by enlarging the mouth of a speaker. In an alternative embodiment the invention may be used to interpret feelings, emotions or gestures of a speaker.

Joint Depth estimation and semantic segmentation from a single image

Adaptive learning and pattern recognition

A method for adaptive learning and pattern recognition that detects and learns significant patterns and pattern semantic in a noisy environment. A predation method of reinforcement and deletion uses stimulus and context activities and the learned semantics knowledge is used to evaluate the semantic context of the pattern.

Vehicle wireless sensing and communication system

Component identification systems

A component 2 to be identifled is placed on a rotating turntable 1. An electronic line scan camera 4 is directed onto turntable 1 and scans along radial lines to obtain a digitised video signal having a multiplicity of levels. Points on each radial line at a predetermined radius are selected to generate an identification signal for the component which is compared with a database of such signals held in a store. When a match is obtained the component is identified. There may be a relatively coarse initial scan covering a full 360 DEG rotation followed by a finer scan over 30 DEG to determine the angular displacement required to orientate component 2 at a required position to enable it to be picked up by an arm 8 or a robot 7. The lighting contrast may be enhanced by suitably positioned lighting 3. ...

RECOGNITION APPARATUS

Distinguishing matrix, capable of learning for groups of analog signals

... 1,046,688. Conditionable translating circuits. STANDARD TELEPHONES & CABLES Ltd. June 11, 1965 [June 12, 1964], No. 24747/65. Addition to 992,170. Heading G4R. An electric translating circuit for identifying sets of n analogue signals comprises a coordinate array of switching elements, Fig. 1 (not shown), having m row conductors, one for each possible set of input signals, (n + 1) column conductors, n for the m analogue signals of a set and an auxiliary (n + 1)th conductor. At every intersection there is a switching element. In the first part of a learning phase for each set the analogue signals of the set to be learned are applied to the column conductors 1 to n and the appropriate row conductor is energized so that the switching elements are set proportionally to the corresponding analogue signals. In the second part of the learning phase the n elements set are interrogated to obtain a signal representing the sum of the squares of the corresponding individual analogue signals which is ...

COMPONENT IDENTIFICATION SYSTEMS

Identifying system

A system for identifying a seal stamp comprises a memory having stored therein two impression reference patterns, namely an original reference pattern and a latest reference pattern, for the seal stamp. An impression pattern read by a reader is collated with the latest reference pattern, and when the patterns are found to match, the read impression is verified and the latest reference pattern in the memory is updated with use of the read impression pattern. If the patterns are found not to match by the collation, the read impression pattern is collated by the original reference pattern. When the two patterns are found to match by this collation, the read impression is verified. The original reference pattern is updated when desired. The system is also useful for identifying other objects such as speech.

Large-scale image tagging using image-to-topic embedding

A method of calculating the relevance of tags applied to images involves receiving an image 314 with associated tags, which may be user-applied tags. The tags are used to create a weighted word vector 218, also known as a soft topic vector, which represents the dominant concept among the keyword tags. Visual features 312 of the image may be used to create an image feature vector 310 which can then be aligned in a common embedding space. The aligned vectors can then be used to calculate a relevancy score for each tag as it pertains to the image. The visual features may be determined with a convolutional neural network. The weighted word and image feature vectors may be aligned using cosine similarity loss. Each tag may be assigned a word vector representation and a weighted average of the word vectors can then be used to generate the weighted word vector.

APPARATUS FOR PATTERN RECOGNITION

Business discovery from imagery

Vehicle lane boundary position

Image recognition of dangerous tools based on deep learning

This invention lies in the field of digital signal processing. It is an image recognition system of different kinds of dangerous tools based on deep learning. The invention consists of the following steps: Firstly, we use web crawler to acquire a sufficient number of pictures from the Internet. Secondly, the data set having been selected and preprocessed is divided into training set and test set. Thirdly, we put the training set of data into the convolutional neural network in batches. By constantly adjusting the parameters of the network such as the base learning rate, the model will reach the optimal performance. Finally, the test set of data is put into the trained neural network and different kinds of tools are recognized with 94.949% accuracy. In brief, this invention can automatically recognize different kinds of dangerous tools without human involvement. Picture acquisition (web crawler) Resize pictures to uniform size (32 x 32) Increase the number of pictures Reshape the picture ...

IMAGE COMPLETION WITH IMPROVED DEEP NEURAL NETWORKS

Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole filling digital content correspond to or include computer-generated content or are photo realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling ...

MOBILE CLEANING ROBOT ARTIFICIAL INTELLIGENCE FOR SITUATIONAL AWARENESS

Attorney Docket: 09945-0362AU1;DP118AU1 Abstract A mobile cleaning robot includes a cleaning head configured to clean a floor surface in an environment, and at least one camera having a field of view that extends above the floor surface. The at least one camera is configured to capture images that include portions of the environment above the floor surface. The robot includes a recognition module is configured to recognize objects in the environment based on the images captured by the at least one camera, in which the recognition module is trained at least in part using the images captured by the at least one camera. The robot includes a storage device is configured to store a map of the environment. The robot includes a control module configured to control the mobile cleaning robot to navigate in the environment using the map and operate the cleaning head to perform cleaning tasks taking into account of the objects recognized by the recognition module. 12274136.docx ...

Method and apparatus for the classification of an article

Multi-modal object signature

Apparatus for and method of pattern recognition and image analysis

Method and system for determining treatments by modifying patient-specific geometrical models

A system for modifying a three-dimensional model based on one or more cardiovascular treatment options for a patient, the system comprising: at least one computer system configured for: creating a reduced order model representing at least a portion of the patient's heart or vasculature based on patient-specific data regarding a geometry of the patient's heart or vasculature; determining a treatment type for a condition in the patient's heart or vasculature; determining a vessel characteristic of a vessel in the portion of the patient's heart or vasculature; obtaining a stored reduced order model parameter of a stent or a bypass graft based on the determined treatment type and the determined vessel characteristic; modifying the reduced order model of the portion of the patient's heart or vasculature to include the obtained reduced order model parameter of the stent or bypass graft; and determining a blood flow rate or pressure within the portion of the patient's heart or vasculature based ...

JOINT DEPTH ESTIMATION AND SEMANTIC LABELING OF A SINGLE IMAGE

Abstract (57) A system and method of performing joint depth estimation and semantic labelling of an image by one or more computing devices, the method comprising the steps of: estimating global semantic and depth layouts of a scene of the image through machine learning by the one or more computing devices; estimating local semantic and depth layouts for respective ones of a plurality of segments of the scene of the image through machine learning by the one or more computing devices; and merging the estimated global semantic and depth layouts with the local semantic and depth layouts by the one or more computing devices to semantically label and assign a depth value to individual pixels in the image.

System and Method for object re-identification

Abstract SYSTEM AND METHOD FOR OBJECT RE-IDENTIFICATION A method (400) of identifying, with a camera, an object in an image (120) of a scene, the 5 method comprising the steps of determining (410) a distinctiveness (411) of each of a plurality of attributes of an object of interest (100), independent of a camera viewpoint; determining (820) a detectability (821) of each of the plurality of attributes based on a relative orientation (541) of a candidate object (130) in the image (120) of the scene; determining (460) a camera setting (461) for viewing the candidate object (130) based on the determined distinctiveness 10 (411) of at least one attribute, so as to increase the detectability of the at least one attribute; and capturing (420) an image of the candidate object with the determined camera setting (461) to determine (440) a confidence (441) that the candidate object is the object of interest. P1 11'71'7 / 091259 0 I Fig. 1A Computer system Fig. 1B P113737 /9208756_1 ...

Method and apparatus for the classification of an article

METHOD FOR RECOGNIZING CHARACTERS

The present invention relates to a method and device for recognizing characters, in which in a learning phase as well as in a subsequent working phase features of character patterns in a number of aspects thereof are classified in a number of groups, during the learning phase the results of these classifications being recorded in a store as statistic frequencies and during the working phase the result of the feature classification of a freshly offered character being utilized in determining, for each class of characters, the probabilities of the features found in this character, and in which the features of a character pattern to be recognized are weighted, the weight attributed to each feature depending on the shape of the pattern and in which, on the basis of the values of these weights, features are selected, the stored statistic frequencies of which are multiplied by the values of the weights and the largest value among the results is utilized for indicating the class.

ADAPTIVE PATTERN RECOGNITION SYSTEM

SELECTIVE APPLICATION OF REPROJECTION PROCESSING ON LAYER SUB-REGIONS FOR OPTIMIZING LATE STAGE REPROJECTION POWER

Optimizations are provided for late stage reprojection processing for a multi-layered scene. A scene is generated, which is based on a predicted pose of a portion of a computer system. A sub-region is identified within one of the layers and is isolated from the other regions in the scene. Thereafter, late stage reprojection processing is applied to that sub-region selectively/differently than other regions in the scene that do not undergo the same late state reprojection processing.

AUTOMATED CORE DESCRIPTION

An image associated with a core sample is received. The image represents a property of the core sample. A plurality of values of at least one image attribute are determined from the received image. A core description of the core sample is determined from a set of core descriptions. The core description describes the property of the core sample. The set of core descriptions are associated with a set of training core samples. Each training core sample has a corresponding core description and is associated with a set of plurality of values. Determining the core description of the core sample is based on a comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples. The core description of the core sample is provided to an output device.

METHODS AND SYSTEMS FOR TEMPERATURE MEASUREMENT WITH MACHINE LEARNING ALGORITHM

A method includes capturing an image, and performing region detection on the captured image. The region detection includes identifying an object represented in the captured image. The method further includes detecting emissivity of the identified object and determining the temperature of the object based on the detected emissivity of the object.

SIMULATING IMAGE CAPTURE

The present disclosure relates to simulating the capture of images. In some embodiments, a document and a camera are simulated using a three-dimensional modeling engine. In certain embodiments, a plurality of images are captured of the simulated document from a perspective of the simulated camera, each of the plurality of images being captured under a different set of simulated circumstances within the three-dimensional modeling engine. In some embodiments, a model is trained based at least on the plurality of images which determines at least a first technique for adjusting a set of parameters in a separate image to prepare the separate image for optical character recognition (OCR).

INTERACTIVE AND ADAPTIVE LEARNING AND NEUROCOGNITIVE DISORDER DIAGNOSIS SYSTEMS USING FACE TRACKING AND EMOTION DETECTION WITH ASSOCIATED METHODS

A system for delivering learning programmes comprising optical sensors for capturing a subject's facial expression, eye movements, point-of-gaze, and head pose during a learning session; a data repository comprising task data entities; a module for estimating the subject's affective and cognitive states using the captured sensory data; and a module for selecting a task data entity for presentment to the subject after each completion of a task data entity based on a probability of the subject's understanding of the associated knowledge; wherein the probability of the subject's understanding is computed using the subject's estimated affective cognitive states. The system can also be applied in neurocognitive disorder diagnosis tests. The subject's affective and cognitive states estimation based on the captured sensory data during a diagnosis test is feedback to the system to drive the course of the test, adaptively change the test materials, and influence the subject's emotions.

METHOD AND SYSTEM FOR DETERMINING TREATMENTS BY MODIFYING PATIENT-SPECIFIC GEOMETRICAL MODELS

Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three- dimensional model representing a portion of the patient's heart based on patient- specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

SYSTEM AND METHOD FOR DETECTING INVISIBLE HUMAN EMOTION

A system and method for emotion detection and more specifically to an image-capture based system and method for detecting invisible and genuine emotions felt by an individual. The system provides a remote and non-invasive approach by which to detect invisible emotion with a high confidence. The system enables monitoring of hemoglobin concentration changes by optical imaging and related detection systems.

ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the WS image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

IMAGE FEATURE DATA EXTRACTION AND USE

... ²An apparatus and method for obtaining image feature data of an image are ²disclosed herein. ²A color histogram of the image is extracted from the image, the extraction of ²the color histogram ²including performing one-dimensional sampling of pixels comprising the image ²in each of a first ²dimension of a color space, a second dimension of the color space, and a third ²dimension of the color ²space. An edge map corresponding to the image is analyzed to detect a pattern ²included in the image. ²In response to a confidence level of the pattern detection being below a pre-²defined threshold, ²extracting from the image an orientation histogram of the image. And identify ²a dominant color of the ²image.² ...

METHOD FOR VISUALIZING BLOOD AND BLOOD-LIKELIHOOD IN VASCULAR IMAGES

Computer-implemented methods for use in improving the diagnostic quality of images, including intravascular ultrasound (IVUS) images, are disclosed. The methods include using a non-linear, probabilistic classifier algorithm to analyze a plurality of spatiotemporal features of RF backscatter and to produce a blood likelihood map or blood probability map that corresponds to the original IVUS image. The methods disclosed herein allow for visualizing both static and dynamic characteristic of a vessel either by producing a transparency modulated color overlay of the blood likelihood map without altering the underlying IVUS image or by processing the IVUS image based upon the blood likelihood map to better distinguish between static and dynamic components of the vessel.

METHOD AND DEVICE FOR AUTOMATIC OBJECT CLASSIFICATION

... 2085012 9220038 PCTABS00160 Procédé et dispositif de classification automatique d'objets comprenant une phase d'apprentissage et une phase d'exécution, combinant en phase d'apprentissage un procédé d'auto-organisation avec le procédé de rétro-propagation du gradient, et réalisant la phase d'exécution suivant la structure du perceptron. Application: reconnaissance de formes pour la robotique ou le contrôle industriel.

VISUALIZATION AND SELF-ORGANIZATION OF MULTIDIMENSIONAL DATATHROUGH EQUALIZED ORTHOGONAL MAPPING

The subject system provides reduced-dimension mapping of pattern data. Mappi ng is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively "topologically correct" low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficie nt at the same time.

Elektrische Schaltung mit Lerncharakter für Gruppen von analogen Signalen

Lernfähige matrixförmige Schaltungsanordnung

Elektrischer Zuordner mit Lerncharakter

Codiervorrichtung

Matrix-Zuordner mit kapazitiver Kopplung zwischen Zeilen und Spalten

Verfahren und Anordnung zum zerstörungsfreien Abfragen einer Kernspeicherlernmatrix

Procédé de classement d'images lumineuses et appareil pour sa mise en oeuvre

Einrichtung zum Bestimmen von Binärmustern

Lernfähige Unterscheidungsmatrix für Gruppen von analogen Signalen

PROCEDURE AND DEVICE FOR MACHINE READING FROM CHARACTERS.

Procedure for the classification of a sample - in particular a sample of a note or a coin - and mechanism for the execution of the procedure.

System and method for recognizing handwriting recognition technology

PATTERN RECOGNITION SYSTEM

THREE-DIMENSIONAL OBJECT RECOGNITION METHOD FOR ANALYTICALLY.

Adaptive Categorisor

Operating system of data

DEVICE INTENDS IN PARTICULAR FOR the IDENTIFICATION STATIC IMAGES BIDIMENTIONNELLES

Parallel processing appts. for information in neutral network - uses variable resistive coupling between processors to allow coupling or isolation, depending on preset law and output states

METHOD FOR MEASURING THE POSITION OF A MOVABLE STRUCTURE

L'invention concerne un procédé de mesure de la position d'une structure mobile dans un référentiel, la structure comprenant une pluralité d'éléments récurrents intrinsèques à la structure, le procédé comprenant les étapes successives suivantes : a) acquisition (20), par des moyens de traitements, d'images de la structure prises simultanément par une pluralité de dispositifs optique, chaque élément récurrent de la structure étant dans le champ de vision d'au moins trois dispositifs optique distincts ; b) extraction (21a,21b) sur chacune des images, par les moyens de traitements, des éléments récurrents et détermination (21c), par les moyens de traitements, de la position desdits éléments récurrents à l'image ; c) calcul (22a) par, les moyens de traitements, d'au moins un indicateur pour chacun des éléments récurrents détectés sur chaque image ; d) identification (22b), par les moyens de traitement, de chacun desdits éléments récurrents en associant un identifiant unique à chacun desdits ...

사진 관리

... 이미지 프로세싱을 위한 방법은 프리트레이닝된 (pre-trained) 딥 (deep) 나선형 망으로부터 다수의 저장된 이미지들의 특징들을 결정하는 단계를 포함한다. 그 방법은 또한 결정된 특징들에 기초하여 그 다수의 저장된 이미지들의 각각의 이미지를 클러스터링하는 단계를 포함한다.

METHOD AND APPARATUS FOR SETTING REGION OF INTEREST

A method and an apparatus for setting a region of interest are disclosed. Specifically, an embodiment of the present invention provides a method and an apparatus for setting a region of interest which can detect a moving object by setting various regions of interest of an image obtained through an image monitoring apparatus, and setting an object moving direction without continuously monitoring the image by a manager to monitor the image. COPYRIGHT KIPO 2019 (112) Interface unit (114) Camera (116) Display unit (122) Segment assignment unit (124) Region of interest setting unit (126) Connection line generating unit (128) Adjustment point generating unit (132) Attribute setting unit (134) Moving direction setting unit (142) Object detecting unit (144) Object direction detecting unit (146) Notification unit ...

Method and apparatus for operating a mobile camera with low power usage

Face identification system and face identification method

A face identification system includes a transmitter, a receiver, a database, an artificial intelligence chip, and a main processor. The transmitter is used for emitting at least one first light signal to an object. The receiver is used for receiving at least one second light signal reflected by the object. The database is used for saving training data. The artificial intelligence chip is coupled to the transmitter, the receiver, and the database for identifying a face image from the object according to the at least one second light signal and the training data. The main processor is coupled to the artificial intelligence chip for receiving a face identification signal generated from the artificial intelligence chip.

SYSTEM AND METHOD FOR SYNTHESIZING PORTRAIT SKETCH FROM PHOTO

The present invention discloses a system and method for synthesizing a portrait sketch from a photo. The method includes: dividing a test photo into multiple equal interval overlapped test photo pieces; determining the match information between each test photo piece and pre-divided photo pieces in multiple training set photos; determining the match information between each test photo piece and pre-divided sketch image pieces in multiple training set sketch images; determining the shape transcendent information of the sketch image which to be synthesized; determining the gray consistency information between two adjacent training set sketch image pieces and the grads consistency information of two adjacent training set sketch image pieces; determining the best match training sketch image piece for each test photo piece based on the match information, the shape transcendent information, the gray consistency information and the grads consistency information; and synthesizing the determined ...

AUTOMATED PROCESSING OF PANORAMIC VIDEO CONTENT USING MACHINE LEARNING TECHNIQUES

The present disclosure provides techniques for capturing, processing, and displaying panoramic content such as video content and image data with a panoramic camera system. In one embodiment, a method for processing panoramic video content may include communicating captured video content to a virtual sensor of a panoramic camera; applying a machine learning algorithm to the captured video content; identifying content of interest information suitable for use by at least one smart application; and executing a smart application in connection with the identified content of interest information. The machine learning algorithm may include at least one of a pattern recognition algorithm or an object classification algorithm. Examples of smart applications include executing modules for automatically panning movement of the camera field of view, creating video content focused on the content of interest, and warning a user of objects, obstacles, vehicles, or other potential hazards in the vicinity ...

Data Processing System with Machine Learning Engine to Provide Output Generating Functions

Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.

Predicting household demographics based on image data

An online system predicts household features of a user, e.g., household size and demographic composition, based on image data of the user, e.g., profile photos, photos posted by the user and photos posted by other users socially connected with the user, and textual data in the user's profile that suggests relationships among individuals shown in the image data of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user's profile data and tags associated with the photos. The online system uses the predictions to build more information about the user and his/her household in the online system, and provide improved and targeted content delivery to the user and the user's household.

Object recognizer and detector for two-dimensional images using bayesian network based classifier

A system and method for determining a classifier to discriminate between two classesobject or non-object. The classifier may be used by an object detection program to detect presence of a 3D object in a 2D image (e.g., a photograph or an X-ray image). The overall classifier is constructed of a sequence of classifiers (or sub-classifiers), where each such classifier is based on a ratio of two graphical probability models (e.g., Bayesian networks). A discrete-valued variable representation at each node in a Bayesian network by a two-stage process of tree-structured vector quantization is discussed. The overall classifier may be part of an object detector program that is trained to automatically detect many different types of 3D objects (e.g., human faces, airplanes, cars, etc.). Computationally efficient statistical methods to evaluate overall classifiers are disclosed. The Bayesian network-based classifier may also be used to determine if two observations (e.g., two images) belong to the ...

Pattern recognition system

A pattern recognition method is disclosed which makes a judgment that when the difference between an object to be inspected and a previously memorized reference object falls within a predetermined value, the object belongs to the same class as the reference object. During the inspection process, the predetermined value is changed in accordance with the results of past judgment.

System and method for providing automated data visualization and modification

A system and method for automated data visualization and modification of visualized data is disclosed. The present invention provides for identifying data points and data types associated with the selected data. Further, one or more visual representations for rendering the selected data are evaluated based on the identified data points and the data types. Yet further, the selected data is optimally visualized based on an identification of a display device type. The present invention further provides for evaluating a theme of visual representations using a real-time lighting information of the real world environment based on identification of the display device type. The selected data is visualized using the evaluated theme of visual representations and the evaluated one or more visual representations. Yet further, the present invention provides for identifying user actions and interpreting inputs from the identified user actions to update or modify visualized data.

Cognitive Browse Operation

A method, system and computer readable medium for performing a cognitive browse operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive browse operation on a corpus of content based upon the cognitive profile, the cognitive browse operation returning cognitive browse results specific to the cognitive profile of the user.

Emoji Understanding in Online Experiences

Understanding emojis in the context of online experiences is described. In at least some embodiments, text input is received and a vector representation of the text input is computed. Based on the vector representation, one or more emojis that correspond to the vector representation of the text input are ascertained and a response is formulated that includes at least one of the one or more emojis. In other embodiments, input from a client machine is received. The input includes at least one emoji. A computed vector representation of the emoji is used to look for vector representations of words or phrases that are close to the computed vector representation of the emoji. At least one of the words or phrases is selected and at least one task is performed using the selected word(s) or phrase(s).

PSEUDO-CT GENERATION FROM MR DATA USING TISSUE PARAMETER ESTIMATION

Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.

ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

System and method for integrated laser scanning and signal processing

There is provided systems and methods for laser scanning of one or more surfaces; in a particular embodiment, laser scanning of concrete. The system includes: at least one laser scanning module for acquiring imaging data comprising one or more distance measurements between the laser scanning module and each of the surfaces, according to a laser scanning modality; and a computing module, including one or more processors, in communication with the laser scanning module, for: aggregating the imaging data; processing and denoising the imaging data; and determining the presence or absence of a condition on each of the surfaces based on the imaging data.

Method and apparatus for providing driver information via audio and video metadata extraction

A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (“DR”) model trained by a machine learning center (“MLC”) coupled to a cloud based network (“CBN”). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (“VOC”). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.

TRAINING IMAGE SIGNAL PROCESSORS USING INTERMEDIATE LOSS FUNCTIONS

In an example method for training image signal processors, a reconstructed image is generated via an image signal processor based on a sensor image. An intermediate loss function is generated based on a comparison of an output of one or more corresponding layers of a computer vision network and a copy of the computer vision network. The output of the computer vision network is based on the reconstructed image. An image signal processor is trained based on the intermediate loss function.

ACCESSION NUMBER CORRECTION SYSTEM AND METHODS FOR USE THEREWITH

An accession number correction system is operable to determine that an accession number of a DICOM image does not link to any corresponding one of a plurality of medical reports. Medical report criteria is generated based on the DICOM image, and a set of medical reports are identified based on the medical report criteria. A computer vision model is trained from a training set of DICOM images, and inference data is generated for the DICOM image by performing at least one inference function utilizing the computer vision model. A selected one of the set of medical reports that corresponds to the DICOM image is determined based on comparing the inference data for the DICOM image to text included in at least one of the set of medical reports. Updated report header data for the selected medical report is generated, and storage of the updated report header data is facilitated.

DEVICE AND METHOD FOR AUTOMATIC MONITORING AND AUTONOMIC RESPONSE

The present invention discloses a device and method for automatic monitoring and autonomic response. The device comprises: a video capture unit, used for capturing and transmitting video in real time; an audio capture unit, used for capturing and transmitting audio in real time; a processing device, used for responding to received video and audio; a processing device, used for responding to the received video and audio, recognizing contents of the video and audio, and issuing instruction; and, a responder, used for receiving the instruction and responding according to the instruction. Compared with the prior art, the present invention allows a camera monitoring system to process automatically and respond autonomically when a certain scenario is monitored, while obviating the need for human intervention.

CONTEXT-BASED PRIORS FOR OBJECT DETECTION IN IMAGES

Context-based priors are utilized in machine learning networks (e.g., neural networks) for detecting objects in images. The likely locations of objects are estimated based on context labels. A machine learning network identifies a context label of an entire image. Based on the, the network selects a set of likely regions for detecting objects of interest in the image. 1. A method of object detection , comprising:identifying a context label of an entire image; andselecting a set of likely regions for detecting objects of interest in the image based at least in part on an identified context label.2. The method of claim 1 , further comprising training a neural network to refine the set of likely regions.3. The method of claim 1 , further comprising creating the context label based at least in part on user input.4. The method of claim 1 , further comprising creating the context label based at least in part on unsupervised learning.5. The method of claim 1 , further comprising generating the set of likely regions based at least in part on the context label.6. The method of claim 1 , further comprising:identifying another context label; andselecting another set of likely regions of detecting objects of interest in the image based at least in part on the other identified context label.7. The method of claim 1 , further comprising training a neural network to determine for each of the likely regions whether an object of interest is present.8. The method of claim 1 , further comprising training a neural network to classify each of the likely regions according to the context label.9. An apparatus for object detection claim 1 , comprising:a memory; and to identify a context label of an entire image; and', 'to select a set of likely regions for detecting objects of interest in the image based at least in part on an identified context label., 'at least one processor coupled to the memory, the at least one processor configured10. The apparatus of claim 9 , in which the at least ...

Fisher vectors meet neural networks: a hybrid visual classification architecture

In an image classification method, a feature vector representing an input image is generated by unsupervised operations including extracting local descriptors from patches distributed over the input image, and a classification value for the input image is generated by applying a neural network (NN) to the feature vector. Extracting the feature vector may include encoding the local descriptors extracted from each patch using a generative model, such as Fisher vector encoding, aggregating the encoded local descriptors to form a vector, projecting the vector into a space of lower dimensionality, for example using Principal Component Analysis (PCA), and normalizing the feature vector of lower dimensionality to produce the feature vector representing the input image. A set of mid-level features representing the input image may be generated as the output of an intermediate layer of the NN.

System and method for superimposed handwriting recognition technology

A system and method is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. The system and method can also process cursive handwriting. Further, the system and method can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of segmentation, character recognition, and language modeling. These three processes occur concurrently through the use of dynamic programming.

Data composite for efficient memory transfer in a behavioral recognition system

Techniques are disclosed for dynamic memory allocation in a behavioral recognition system. According to one embodiment of the disclosure, one or more variable-sized chunks of memory is allocated from a device memory for a memory pool. An application allocates at least one of the chunks of memory from the memory pool for processing a plurality of input data streams in real-time. A request to allocate memory from the memory pool for input data is received. Upon determining that one of the chunks is available in the memory pool to store the input data, the chunk is allocated from the memory pool in response to the request.

IMAGE PROCESSING METHOD AND APPARATUS FOR X-RAY IMAGING DEVICE

This disclosure presents an image processing method and related X-ray imaging device The method comprises: calculating a relative displacement between two first images that are already in auto registration as a first displacement vector; calculating a difference between position information fed back by a position sensor on the X-ray imaging device when imaging exposure is performed on the two first images respectively as a second displacement vector; calculating a first error of the first displacement vector relative to the second displacement vector; calculating a registration level corresponding to the first error in accordance with a pre-stored training model which is a mathematical distribution model of second errors between a plurality of third displacement vectors and a plurality of corresponding fourth displacement vectors; and labeling the registration level on the two first images that are already in auto registration.

Digital platform to identify health conditions and therapeutic interventions using an automatic and distributed artificial intelligence system

This disclosure is directed to method and system for automatic, distributed, computer-aided, and intelligent data collection/analytics, health monitoring, health condition identification, and patient preventive/remedial health advocacy. The system integrates (1) distributed patient health data collection devices, (2) centralized or distributed data servers running various intelligent and predictive data analytics engines for health screening, assessment, patient health condition identification, and patient preventive/remedial health advocacy, 3) specifically designed data structures including quantized health indicator vectors, patient health condition identification matrices and patient health condition vectors, (4) portal servers configured to interface with (5) distributed physician terminal devices and (6) distributed patient terminal devices for delivering health condition identification, health interventions and patient preventive/remedial health advocacy, and for monitoring and tracking ...

Methods And Systems For Determining Optimal Features For Classifying Patterns Or Objects In Images

Provided are methods for determining optimal features for classifying patterns or objects. Also provided are methods for image analysis. Further provided are methods for image searching. 1. A method for image analysis comprising:receiving at least one image; anddetermining features from the at least one image by classifying the at least one image using a trained classifier wherein the trained classifier utilizes one or more independent components.2. The method of claim 1 , wherein the at least one image is of a non-natural scene.3. The method of claim 2 , wherein the at least one image is one of claim 2 , a color retinal image claim 2 , a monochrome retinal image claim 2 , a color stereo pair claim 2 , a monochrome stereo pair claim 2 , an x-ray image claim 2 , a computer-aided tomographic (CAT) scan image claim 2 , an angiogram image claim 2 , a fMRI image claim 2 , or a PET image.4. The method of claim 1 , further comprising training the classifier claim 1 , rein training the classifier comprises:presenting, to the classifier, a pre-classified image;separating the pre-classified image into a plurality of channels;determining a set of samples from the plurality of channels; andfor each channel, performing an Independent Component Analysis (ICA) on the set of samples resulting in a set of independent components.5. The method of claim 1 , wherein the trained classifier is one of claim 1 , a k-Nearest Neighbors classifier claim 1 , a linear discriminant classifier claim 1 , a quadratic discriminant classifier claim 1 , or a support vector machine.6. The method of claim 4 , wherein the ICA uses two dimensional samples from two-dimensional channels7. The method of claim 4 , wherein the ICA uses three dimensional samples from three-dimensional channels.8. The method of claim 4 , wherein the plurality of channels comprises one or more of claim 4 , red claim 4 , green claim 4 , blue claim 4 , a vectorial representation of multichannel data claim 4 , color opponency ...

COMPUTER-IMPLEMENTED METHOD, A COMPUTER PROGRAM PRODUCT AND A COMPUTER SYSTEM FOR IMAGE PROCESSING

The present description refers in particular to a computer-implemented method, a computer program product and a computer system for image processing, the method comprising: receiving at least one user image; identifying a plurality of image classification elements of the user image by: assigning an initial classification to the user image, wherein the initial classification is based on temporal data associated with the user image; determining at least one image label that globally describes content of the user image; calculating a label correctness value for each image label; recognizing at least one image component of the user image; calculating a component correctness value for each image component; correlating the image label and the image component using the label correctness value and the component correctness value, whereby a correlated image label and a correlated image component are identified; applying a rule to determine a category of the user image, wherein the rule is based on at least one of the following: the temporal data, the correlated image label and the correlated image component; and producing a final classification of the user image including the following image classification elements: the initial classification, the correlated image label, the correlated image component, and the category. 1. A computer-implemented method for image processing , the method comprising:{'b': 701', '506, 'receiving () at least one user image ();'} assigning an initial classification to the user image, wherein the initial classification is based on temporal data associated with the user image;', 'determining at least one image label that globally describes content of the user image;', 'calculating a label correctness value for each image label;', {'b': 705', '508', '510, 'recognizing () at least one image component (, ) of the user image;'}, {'b': 508', '510, 'calculating a component correctness value for each image component (, );'}, {'b': 708', '508', '510, ' ...

USING HIGHER ORDER STATISTICS TO ESTIMATE PIXEL VALUES IN DIGITAL IMAGE PROCESSING TO IMPROVE ACCURACY AND COMPUTATION EFFICIENCY

A method, system and computer program product for improving accuracy and computation efficiency in interpolation, upsampling and color channel estimation. A Bayesian estimator used to estimate the value of a pixel in an image is constructed using measurements of high-order (e.g., 3rd, 4th, 5th) statics for nearby points in natural images. These measurements reveal highly systematic statistical regularities that were ignored from the prior algorithms due to their restrictive measurements and assumptions. As a result, the accuracy in interpolation, upsampling and color channel prediction is improved. Furthermore, the process for constructing a Bayesian estimator is simpler and more direct by storing in a table the mean value of the pixel value to be estimated for each combination of values of nearby points in training samples. As a result of having a simpler and more direct approach than existing methods, the computational efficiency is improved. 1. A method of determining an optimum candidate pixel value , the method comprising:i. identifying a first plurality of candidate pixels in a training image wherein each of said plurality of candidate pixels is proximate to a first identical neighbor pixel group;ii. for each of said first plurality of candidate pixels, determining a candidate pixel value thereby obtaining a first plurality of candidate pixel values;iii. determining a plurality of statistical moments for said first plurality of candidate pixel values; and a first optimum candidate pixel value corresponding to said first identical neighbor pixel group, said first optimum candidate pixel being determined based on said plurality of statistical moments, and', 'information indicative of said plurality of statistical moments., 'iv. storing in memory a table including at least one of2. The method of claim 1 , wherein:step i further comprises identifying a plurality of additional candidate pixels in a plurality of additional training images;step ii further comprises, ...

CONTINUOUS CHARTING OF NON-UNIFORMITY SEVERITY FOR DETECTING VARIABILITY IN WEB-BASED MATERIALS

A computerized inspection system is described for detecting the presence of non-uniformity defects in a manufactured web material and for providing output indicative of a severity level of each defect. The system provides output that provides the severity levels of the non-uniformity defects in real-time on a continuous scale. Training software processes a plurality of training samples to generate a model, where each of the training samples need only be assigned one of a set of discrete rating labels for the non-uniformity defects. The training software generates the model to represent a continuous ranking of the training images, and the inspection system utilizes the model to compute the severity levels of the web material on a continuous scale in real-time without limiting the output to the discrete rating labels assigned to the training samples. 1. A method comprising:executing software on a computer to extract features from each of a plurality of training images by computing a numerical descriptor for each of the training images from pixel values of the respective training image, wherein each of the images has been assigned one of a set of discrete rating labels for a non-uniform defect present within the training images;processing the numerical descriptors of the training images with the rating software to compute a continuous ranking of the training images based on the discrete rating labels assigned to the training images; andprocessing a new image captured from a manufactured web material to extract features from the new image and compute a severity level of the non-uniform defect for the web based on the continuous ranking of the training image.2. The method of claim 1 , further comprising presenting a user interface to output the severity level to a user.3. The method of claim 2 , wherein presenting a user interface comprising updating a chart to graph the severity level of the non-uniform defect for the web material over time.4. The method of claim 2 , ...

SYSTEM AND METHOD FOR AUTOMATIC LANDMARK LABELING WITH MINIMAL SUPERVISION

A system and method for estimating a set of landmarks for a large image ensemble employs only a small number of manually labeled images from the ensemble and avoids labor-intensive and error-prone object detection, tracking and alignment learning task limitations associated with manual image labeling techniques. A semi-supervised least squares congealing approach is employed to minimize an objective function defined on both labeled and unlabeled images. A shape model is learned on-line to constrain the landmark configuration. A partitioning strategy allows coarse-to-fine landmark estimation. 120.-. (canceled)21. A method comprising:identifying a whole warped image as a first level patch;obtaining initial landmark locations from the first level patch;iteratively partitioning the whole warped image region into smaller child patches; andrefining landmark estimations from the initial landmark locations to establish accurate landmark labeling based on resultant patch appearance.22. The method of claim 21 , wherein obtaining the initial landmark locations comprises importing the initial landmark locations into a computer in response to algorithmic software.23. The method of claim 22 , comprising manually labeling the initial landmark locations.24. The method of claim 21 , comprising propagating the initial landmark locations from a first set of images to a second set of images that is larger than the first set of images using a computer in response to algorithmic software.25. The method of claim 24 , wherein the first set of images is within an object class and the second sets of images is within the object class.26. The method of claim 24 , wherein the first set of images comprises from about 1% to about 3% of the second set of images.27. The method of claim 21 , comprising performing landmark estimations including minimizing an objective function defined as a summation of pairwise L2 distances between warped images.28. The method of claim 27 , wherein the pairwise L2 ...

SYSTEM AND METHOD FOR SYNTHESIZING PORTRAIT SKETCH FROM A PHOTO

The present invention discloses a system and method for synthesizing a portrait sketch from a photo. The method includes: dividing the photo into a set of photo patches; determining first matching information between each of the photo patches and training photo patches pre-divided from a set of training photos; determining second matching information between each of the photo patches and training sketch patches pre-divided from a set of training sketches; determining a shape prior for the portrait sketch to be synthesized; determining a set of matched training sketch patches for each of the photo patches based on the first and the second matching information and the shape prior; and synthesizing the portrait sketch from the determined matched training sketch patches. 1. A computer-implemented method for synthesizing a portrait sketch from a photo , comprising:dividing the photo into a set of overlapping photo patches;determining first matching information between each of the photo patches and training photo patches pre-divided from a set of training photos;determining second matching information between each of the photo patches and training sketch patches pre-divided from a set of training sketches;determining a shape prior for the portrait sketch to be synthesized;determining a set of matched training sketch patches for each of the photo patches based on the first and the second matching information and the shape prior; andsynthesizing the portrait sketch from the determined matched training sketch patches.2. A method according to claim 1 , wherein the first matching information includes a squared difference distance between each of the photo patches and the corresponding training photo patch claim 1 , andthe second matching information includes a squared difference distance between each of the photo patches and the corresponding training sketch patch.3. A method according to claim 1 , wherein the determining a set of matched training sketch patches further ...

VISION-GUIDED ROBOTS AND METHODS OF TRAINING THEM

Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques. 1. A method of training a robot to complete a specified task comprising selection of an object , the robot having a machine-vision system for capturing images within a camera field of view , the method comprising:(a) selecting, by the robot, an object in the field of view and displaying the selection on a robot-controlled visual display, the displayed selection including a camera view of the selected object and a robot-generated object outline overlaid thereon;(b) based on the display, providing feedback to the robot indicating whether the selection is correct; and(c) repeating steps (a) and (b) until the selection is correct.2. The method of claim 1 , wherein the selection is based on at least one of mechanical or visual input provided by a user.3. The method of claim 2 , further comprising bringing a robot appendage into physical contact with an object of interest so as to provide the mechanical input.4. The method of claim 2 , further comprising bringing an object of interest into the camera field of view so as to provide the visual input.5. The method of claim 1 , wherein the selection is based on a visual model and at least one associated object representation stored in memory.6. The method of claim 5 , wherein the object representation comprises at least one of an image template claim 5 , coordinates and texture descriptors associated with object keypoints claim 5 , or parameters associated with an object shape.7. A vision-guided robot trainable claim 5 , via interactions with a human trainer claim 5 , to complete a specified task comprising selection of an object claim 5 , the robot comprising:a machine-vision system comprising at least one camera for capturing images ...

METHOD OF OPTIMAL OUT-OF-BAND CORRECTION FOR MULTISPECTRAL REMOTE SENSING

A method of image processing. An expected band-averaged spectral radiances image vector is simulated from training hyperspectral data and at least one filter transmittance function corresponding to the at least one optical filter. A simulated measured band-averaged spectral radiances image vector is simulated from the training hyperspectral data and the at least one transmittance function. A realistic measured band-averaged spectral radiances image vector is provided from at least one optical filter. A cross-correlation matrix of the expected band-averaged spectral radiances image vector and the realistic measured band-averaged spectral radiances image vector is calculated. An auto-correlation matrix of the simulated measured band-averaged spectral radiances image vector is calculated. An optimal out-of-band transform matrix is generated by matrix-multiplying the cross-correlation matrix and an inverse of the auto-correlation matrix. A realistic recovered band-averaged spectral radiances image vector is generated by matrix-multiplying the optimal out-of-band transform matrix and the realistic measured band-averaged spectral radiances image vector, the realistic recovered band-averaged spectral radiances image vector being free of out-of-band effects. 1. The method comprising:providing training hyperspectral data and at least one optical filter,simulating expected band-averaged spectral radiances image vector from the training hyperspectral data and at least one filter transmittance function corresponding to the at least one optical filter;simulating measured band-averaged spectral radiances image vector from the training hyperspectral data and the at least one transmittance function;providing a cost function comprising total errors between the simulated expected band-averaged spectral radiances image vector and the simulated measured band-averaged spectral radiances image vector, and an out-of-band transform matrix parameter;optimizing the cost function by applying ...

SEMANTIC REPRESENTATION MODULE OF A MACHINE LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames. 1receiving input data describing one or more objects detected in the scene, wherein the input data includes at least a classification for each of the one or more objects;identifying one or more primitive events, wherein each primitive event provides a semantic value describing a behavior engaged in by at least one of the objects depicted in the sequence of video frames and wherein each primitive event has an assigned primitive event symbol;generating, for one or more objects, a primitive event symbol stream which includes the primitive event symbols corresponding to the primitive events identified for a respective object;generating, for one or more objects, a phase space symbol stream, wherein the phase space symbol stream describes a trajectory for a respective object through a phase space domain;combining the primitive event symbol stream and the phase space symbol stream for each respective object to form a first vector representation of that object; andpassing the first vector representations to a machine learning engine configured to identify patterns of behavior for each object classification from the first vector representation.. A method for processing data describing a scene depicted in a sequence of video frames, the method comprising: This application is a continuation of U.S. patent ...

IMAGE SHARPNESS CLASSIFICATION SYSTEM

A method for predicting whether a test image () is sharp or blurred includes the steps of: training a sharpness classifier () to discriminate between sharp and blurred images, the sharpness classifier () being trained based on a set of training sharpness features () computed from a plurality of training images (), the set of training sharpness features () for each training image () being computed by (i) resizing each training image () by a first resizing factor; (ii) identifying texture regions () in the resized training image; and (iii) computing the set of sharpness features in the training image () from the identified texture regions; and applying the trained sharpness classifier () to the test image () to determine if the test image () is sharp or blurred based on a set of test sharpness features () computed from the test image (), the set of test sharpness features () for each test image () being computed by (i) resizing the test image () by a second resizing factor that is different than the first resizing factor; (ii) identifying texture regions () in the resized test image; and (iii) computing the set of sharpness features in the test image () from the identified texture regions. 1. A method for determining if a test image is either sharp or blurred , the method comprising the steps of:training a sharpness classifier to discriminate between sharp and blurred images, the sharpness classifier being trained based on a set of training sharpness features computed from a plurality of training images, the set of training sharpness features for each training image being computed by (i) resizing each training image by a first resizing factor; (ii) identifying texture regions in the resized training image; and (iii) computing the set of sharpness features in the training image from the identified texture regions; andapplying the trained sharpness classifier to the test image to determine if the test image is sharp or blurred based on a set of test sharpness features ...

SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, OUTPUT APPARATUS, OUTPUT METHOD, AND PROGRAM

There is provided a signal processing apparatus including a learning unit that learns a plurality of base signals of which coefficients become sparse, using a cost function including a term showing a correspondence between the coefficients, such that signals are represented by a linear operation of the plurality of base signals. 1. A signal processing apparatus comprising:a learning unit that learns a plurality of base signals of which coefficients become sparse, using a cost function including a term showing a correspondence between the coefficients, such that signals are represented by a linear operation of the plurality of base signals.2. The signal processing apparatus according to claim 1 ,wherein the cost function includes a term that shows a spatial correspondence between the coefficients.3. The signal processing apparatus according to claim 1 ,wherein the cost function includes a term that shows a temporal correspondence between the coefficients.4. The signal processing apparatus according to claim 1 ,wherein the learning unit learns the plurality of base signals of individual color channels, using the cost function including the term showing the correspondence between the coefficients of the individual color channels, such that the signals of the individual color channels are represented by the linear operation.5. The signal processing apparatus according to claim 1 , further comprising:a band dividing unit that divides bands of the signals and generates the signals of the individual bands,wherein the learning unit learns the plurality of base signals of the individual bands, using the cost function including the term showing the correspondence between the coefficients of the individual bands, such that the signals of the individual bands generated by the band dividing unit are represented by the linear operation.6. The signal processing apparatus according to claim 1 ,wherein the learning unit learns the plurality of base signals using the cost function, ...

SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, OUTPUT APPARATUS, OUTPUT METHOD, AND PROGRAM

There is provided a signal processing apparatus including a learning unit that learns a plurality of base signals of which coefficients become sparse, for each of features of signals, such that the signals are represented by a linear operation of the plurality of base signals. 1. A signal processing apparatus comprising:a learning unit that learns a plurality of base signals of which coefficients become sparse, for each of features of signals, such that the signals are represented by a linear operation of the plurality of base signals.2. The signal processing apparatus according to claim 1 ,wherein the features are features of bands of the signals.3. The signal processing apparatus according to claim 1 ,wherein the features are features of scenes or photographing environments of the signals.4. The signal processing apparatus according to claim 1 ,wherein the features are features of positions of a depth direction of images as the signals.5. The signal processing apparatus according to claim 1 ,wherein the features are features of spatial positions of images as the signals.6. The signal processing apparatus according to claim 1 ,wherein the features are features of time changes of moving images as the signals.7. The signal processing apparatus according to claim 1 ,wherein the features are features of terminals corresponding to the signals.8. A signal processing method performed by a signal processing apparatus claim 1 , the signal processing method comprising:learning a plurality of base signals of which coefficients become sparse, for each of features of signals, such that the signals are represented by a linear operation of the plurality of base signals.9. A program for causing a computer to function as a learning unit that learns a plurality of base signals of which coefficients become sparse claim 1 , for each of features of signals claim 1 , such that the signals are represented by a linear operation of the plurality of base signals.10. An output apparatus ...

Systems and Methods for Visual Object Matching

Systems and methods for improving visual object recognition by analyzing query images are disclosed. In one example, a visual object recognition module may determine query images matching objects of a training corpus utilized by the module. Matched query images may be added to the training corpus as training images of a matched object to expand the recognition of the object by the module. In another example, relevant candidate image corpora from a pool of image data may be automatically selected by matching the candidate image corpora against user query images. Selected image corpora may be added to a training corpus to improve recognition coverage. In yet another example, objects unknown to a visual object recognition module may be discovered by clustering query images. Clusters of similar query images may be annotated and added into a training corpus to improve recognition coverage. 121.-. (canceled)22. A computer-implemented method comprising:obtaining a query image that has been submitted by a user of an image search system;obtaining, for the query image, (i) one or more training images of a corpus of training images that the image search system has identified as responsive to the query image, and, (ii) for each one of the one or more training images that the image system has identified as responsive to the query image, a similarity score that reflects a level of similarity between the query image and the respective training image; anddetermining whether to add the query image to the corpus of training images based on the one or more similarity scores for the query image.23. The method of claim 22 , wherein obtaining one or more training images that the image search system has identified as responsive to the query image comprises:matching the query image to one or more objects using a visual object recognition module; andidentifying the one or more training images based on the one or more objects.24. The method of claim 23 , wherein matching the query image to ...

METHODS FOR AUTOMATICALLY GENERATING A CARD DECK LIBRARY AND MASTER IMAGES FOR A DECK OF CARDS, AND A RELATED CARD PROCESSING APPARATUS

A method of automatically generating a calibration file for a card handling device comprises automatically generating a calibration file stored in memory of a main control system for a card handling device. Automatically generating the calibration file comprises identifying at least one parameter associated with a rank area around a rank of the at least a portion of the card, identifying at least one parameter associated with a suit area around a suit of the at least a portion of the card, and storing the at least one parameter associated with the rank area and the at least one parameter associated with the suit area in the calibration file. Additionally, a method of automatically generating deck libraries for one or more decks of cards comprises automatically generate a plurality of master images for the cards of the first deck type using the parameters from the calibration file. 1. A method of automatically generating a calibration file for a card handling device , the method comprising:capturing a raw image from at least a portion of a card passing through a card handling device; and identifying at least one parameter associated with a rank area around a rank of the at least a portion of the card;', 'identifying at least one parameter associated with a suit area around a suit of the at least a portion of the card; and', 'storing the at least one parameter associated with the rank area and the at least one parameter associated with the suit area in the calibration file., 'using a processor, automatically generating a calibration file stored in memory of a main control system of the card handling device, wherein automatically generating the calibration file comprises2. The method of claim 1 , wherein automatically generating the calibration file comprises identifying a location and at least one parameter associated with a region of interest that is relatively larger than the rank area and the suit area claim 1 , the method further comprising storing the location ...

Method and System for Bone Segmentation and Landmark Detection for Joint Replacement Surgery

A method and system for automatic bone segmentation and landmark detection for joint replacement surgery is disclosed. A 3D medical image of at least a target joint region of a patient is received. A plurality bone structures are automatically segmented in the target joint region of the 3D medical image and a plurality of landmarks associated with a joint replacement surgery are automatically detected in the target joint region of the 3D medical image. The boundaries of segmented bone structures can then be interactively refined based on user inputs. 1. A method for bone segmentation and landmark detection for joint replacement surgery , comprising:receiving a 3D medical image of at least a target joint region of a patient;automatically segmenting a plurality bone structures in the target joint region of the 3D medical image; andautomatically detecting a plurality of landmarks associated with a joint replacement surgery in the target joint region of the 3D medical image.2. The method of claim 1 , wherein the target joint region is a knee region.3. The method of claim 2 , wherein automatically segmenting a plurality bone structures in the target joint region of the 3D medical image comprises;automatically segmenting a femur, tibia, fibula, and patella in the 3D medical image.4. The method of claim 3 , wherein automatically detecting a plurality of landmarks associated with a joint replacement surgery in the target joint region of the 3D medical image comprises:automatically detecting a femur medial most distal, femur lateral most distal, femur lateral posterior condyle point, femur anterior cortex point, femur medial posterior condyle point, femoral head, and ankle center in the 3D medical image.5. The method of claim 1 , wherein automatically segmenting a plurality bone structures in the target joint region of the 3D medical image comprises:independently segmenting each of the plurality of bone structures in the 3D medical image.6. The method of claim 1 , wherein ...

Method and appartaus for data efficient semantic segmentation

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

EXTRACTING CARD DATA WITH WEAR PATTERNS

Embodiments herein provide computer-implemented techniques for allowing a user computing device to extract financial card information using optical character recognition (“OCR”). Extracting financial card information may be improved by applying various classifiers and other transformations to the image data. For example, applying a linear classifier to the image to determine digit locations before applying the OCR algorithm allows the user computing device to use less processing capacity to extract accurate card data. The OCR application may train a classifier to use the wear patterns of a card to improve OCR algorithm performance. The OCR application may apply a linear classifier and then a nonlinear classifier to improve the performance and the accuracy of the OCR algorithm. The OCR application uses the known digit patterns used by typical credit and debit cards to improve the accuracy of the OCR algorithm. 1. A computer-implemented method for training systems to recognize wear patterns on cards , comprising:receiving, by one or more computing devices, an image of a card from a camera, the card having digits thereon that are worn according to a wear pattern;performing, by the one or more computing devices, a classification algorithm the image;providing for display, by the one or more computing devices, results of the performance of the classification algorithm along with a revision indication control to provide revisions of the results of the performance of the classification algorithm;receiving, by the one or more computing devices, a revision of the results of the performance of the classification algorithm, wherein the revision comprises an identification of a wear pattern of the digits and the revision of the results was input using the revision indication control;categorizing, by the one or more computing devices, the wear pattern of the digits indicated by the revision; andcreating, by the one or more computing devices, a wear pattern data transformation for ...

SEMI-SUPERVISED METHOD FOR TRAINING MULTIPLE PATTERN RECOGNITION AND REGISTRATION TOOL MODELS

A system and method for training multiple pattern recognition and registration models commences with a first pattern model. The model is trained from multiple images. Composite models can be used to improve robustness or model small differences in appearance of a target region. Composite models combine data from noisy training images showing instances of underlying patterns to build a single model. A pattern recognition and registration model is generated that spans the entire range of appearances of the target pattern in the set of training images. The set of pattern models can be implemented as either separate instances of pattern finding models or as a pattern multi-model. The underlying models can be standard pattern finding models or pattern finding composite models, or a combination of both. 1. A method for training a pattern recognition and registration model in a machine vision system , the method comprising the steps of:providing one or more initial training images having a region specifying a pattern to be trained, the one or more training images being provided from a database containing a plurality of training images;training a first pattern model from the one or more initial training images;iterating over the remaining images and selecting the high scoring images as input to model training; andoutputting a trained pattern model that includes features common to a predetermined number of the plurality of training images, the trained pattern model being ii different from the first pattern model.2. The method as set forth in wherein the step of iterating includes running the first pattern model to score each image.3. The method as set forth in wherein the first pattern model is trained using a first set of training parameters and a second pattern model is trained using a second set of training parameters.41. The method as set forth in wherein the metric used to score the images consists of calculating a combined score which is initialized to zero claim 1 , ...

Display Device, Vehicle Controller, Transmitter, And Travelling Assistance System

A display device includes: an information acquisition unit communicating with an outside to acquire absence region information identifying an absence region in which an obstacle is presumed to be absent; and a display unit displaying the absence region, which is acquired by the information acquisition unit, in a state of superimposing the absence region on a map. A vehicle controller includes: an information acquisition unit communicating with an outside to acquire absence region information identifying an absence region in which an obstacle is presumed to be absent; and a vehicle control unit performing vehicle control based on the absence region. A transmitter includes: a sensor detecting an obstacle; an information creation unit creating absence region information identifying an absence region based on a result detected by the sensor; and a transmission unit transmitting the absence region information. In addition, a travelling assistance system includes the display device and the transmitter. 1. A display device comprising:an information acquisition unit that communicates with an outside to acquire absence region information identifying an absence region in which an obstacle is presumed to be absent; anda display unit that displays the absence region, which is acquired by the information acquisition unit, in a state of superimposing the absence region on a map.2. The display device according to claim 1 , wherein:the absence region information includes a positional accuracy of the absence region; andthe display unit selects and displays an area, where a probability of the area being the absence region is equal to or higher than a threshold value, in the absence region based on the positional accuracy.3. The display device according to claim 1 , further comprising:a prediction unit that predicts the absence region subsequent to a moment at which the absence region information is created, based on a positional change of the absence region as time elapses,wherein ...

SEARCHING UNTAGGED IMAGES WITH TEXT-BASED QUERIES

In various implementations, a personal asset management application is configured to perform operations that facilitate the ability to search multiple images, irrespective of the images having characterizing tags associated therewith or without, based on a simple text-based query. A first search is conducted by processing a text-based query to produce a first set of result images used to further generate a visually-based query based on the first set of result images. A second search is conducted employing the visually-based query that was based on the first set of result images received in accordance with the first search conducted and based on the text-based query. The second search can generate a second set of result images, each having visual similarity to at least one of the images generated for the first set of result images. 1. A non-transitory computer storage medium storing computer-useable instructions that , when used by one or more computing devices , cause the one or more computing devices to perform operations comprising:receiving a text-based query for searching a first plurality of images, wherein each of at least some of the first plurality of images being associated with one or more characterizing tags;receiving, in accordance with a first search conducted based on the text-based query, a first set of result images from the first plurality of images, each result image in the first set having at least one characterizing tag corresponding to the text-based query;generating a visually-based query using one or more images from the first set of result images received in accordance with the first search conducted based on the text-based query, the visually-based query generated for searching at least one of: the first plurality of images and a second plurality of images; andreceiving, in accordance with a second search conducted based on the visually-based query, a second set of result images from the at least one of: the first plurality of images and the ...

System and method for automatic pulmonary embolism detection

A system and method for detecting pulmonary embolisms in a subject's vasculature are provided. In some aspects, the method includes acquiring a set of images representing a vasculature of the subject, and analyzing the set of images to identify pulmonary embolism candidates associated with the vasculature. The method also includes generating, for identified pulmonary embolism candidates, image patches based on a vessel-aligned image representation, and applying a set of convolutional neural networks to the generated image patches to identify pulmonary embolisms. The method further includes generating a report indicating identified pulmonary embolisms.

EXTRACTING SALIENT FEATURES FROM VIDEO USING A NEUROSYNAPTIC SYSTEM

Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input sequence of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes. 1. A method comprising:receiving an input sequence of image frames, wherein each image frame comprises at least one pixel channel representing a dimension of the input sequence of image frames; and generating a corresponding multi-scale data structure by spatially subsampling corresponding neural spiking data representing pixel intensity of each pixel of the pixel channel at different subsampling scales;', 'generating at least one corresponding saliency map by extracting at least one salient feature from the corresponding multi-scale data structure;', 'normalizing resolution of each corresponding saliency map;', 'applying a Gaussian smoothing operator to each corresponding saliency map to suppress speckles and enhance centers indicating salient features; and', 'merging each saliency map corresponding to each pixel channel into a combined saliency map representing estimated visual saliency for the input sequence of image frames., 'for each pixel channel of each image frame, 'utilizing one or more neurosynaptic core circuits to estimate visual saliency for the input sequence of image frames, wherein the one or more ...

COMPUTER VISION AS A SERVICE

A computer vision service includes technologies to, among other things, analyze computer vision or learning tasks requested by computer applications, select computer vision or learning algorithms to execute the requested tasks based on one or more performance capabilities of the computer vision or learning algorithms, perform the computer vision or learning tasks for the computer applications using the selected algorithms, and expose the results of performing the computer vision or learning tasks for use by the computer applications. 132-. (canceled)33. A platform for providing machine learning algorithm services to user-oriented computer applications , the platform comprising:an application-algorithm interface, comprising a plurality of instructions embodied in one or more non-transitory machine accessible storage media, to determine application parameters to perform a machine learning task on digital content, the machine learning task received from a computer application, at least one of the application parameters indicating a characteristic of the digital content;an algorithm capabilities interface to, based on the application parameters, identify candidate machine learning algorithms to perform the machine learning task on the digital content based on the application parameters;a performance interface to evaluate a capability of each of the candidate machine learning algorithms to perform the machine learning task on the digital content, the performance capability determined at least in part by the characteristic of the digital content; andan algorithm organization framework to organize the candidate machine learning algorithms according to a plurality of different levels of abstraction, and wherein the platform is to select a level of abstraction based on the machine learning task.34. The platform of claim 33 , wherein the platform is to select a machine learning algorithm of the candidate machine learning algorithms based on the evaluating of the capability of ...

Image Retrieval with Deep Local Feature Descriptors and Attention-Based Keypoint Descriptors

Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application. 120.-. (canceled)21. A computing system configured to perform image retrieval , the computing system comprising:one or more processors; and [ a feature extraction portion configured to extract a plurality of local feature descriptors from the input image; and', 'an attention portion configured to determine a plurality of attention scores respectively for the plurality of local feature descriptors; and, 'a machine-learned image descriptor model configured to determine a set of keypoint descriptors for an input image, the machine-learned image descriptor model comprising, obtaining a query image;', 'processing the query image using the feature extraction portion of the machine-learned image descriptor model to obtain, as an output of the feature extraction portion, a first plurality of local feature descriptors for the query image;', 'processing the first plurality of local feature descriptors for the query image using the attention portion of the machine-learned image descriptor model to obtain, as an output of the attention portion, a ...

ATTRIBUTE RECOGNITION SYSTEM, LEARNING SERVER AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM

An attribute recognition system has a person face detection circuitry to detect a suitable person or face for recognition of at least one attribute from persons or faces captured in frame images input from at least one camera to capture a given capture area, an identification information assignment circuitry to identify the persons or faces captured in the frame images having been subjected to the detection by the person face detection circuitry so as to assign an identification information to each identified person or face, and an attribute recognition circuitry to recognize the attribute of a person or face assigned with the identification information, only if the person or face is yet without being subjected to recognition of the attribute, and at the same time if the person or face has been detected by the person face detection circuitry as a suitable person or face for the recognition of the attribute. 1. An attribute recognition system comprising:a person face detection circuitry configured to detect a suitable person or face for recognition of at least one attribute from persons or faces captured in frame images input from at least one camera to capture a given capture area;an identification information assignment circuitry configured to identify the persons or faces captured in the frame images having been subjected to the detection by the person face detection circuitry so as to assign an identification information to each identified person or face; andan attribute recognition circuitry configured to recognize the at least one attribute of a person or face assigned with the identification information, only if the person or face is yet without being subjected to recognition of the at least one attribute, and at the same time if the person or face has been detected by the person face detection circuitry as a suitable person or face for the recognition of the at least one attribute.2. The attribute recognition system according to claim 1 ,wherein the person ...

SCENE AND USER-INPUT CONTEXT AIDED VISUAL SEARCH

Provided is a technique for determining a context of an image and an object depicted by the image based on the context. A trained context classification model may determine a context of an image, and a trained object recognition model may determine an object depicted by the image based on the image and the context. Provided is also a technique for determining an object depicted within an image based on an input location of an input detected by a display screen. An object depicted within an image may be detected based on a distance in feature space between an image feature vector of an image and a feature vector of the object, and a distance in pixel-space between an input location of an input and location of the object within the image. 1. A tangible , non-transitory computer-readable medium storing computer program instructions that when executed by one or more processors effectuate operations comprising: the input caused the image to be captured,', 'the input location is a location in pixel-space of the image, and', 'the image depicts a first object located at a first location in the image;, 'obtaining, with a computer system, (i) an image captured by a mobile computing device and (ii) coordinates indicating an input location of an input detected on a display screen of the mobile computing device, wherein each image of the training data set is labeled with an object identifier,', 'each object identifier indicates an object in an object ontology depicted by a corresponding image, and', 'the object ontology comprises the first object;, 'obtaining, with the computer system, a computer-vision object recognition model trained using a training data set comprising images depicting objects, wherein a first distance in a feature space of the computer-vision object recognition model between an image feature vector of the image and a first feature vector of the first object in the computer-vision object recognition model; and', 'a first distance in the pixel-space of the ...

Determining an item that has confirmed characteristics

In various example embodiments, a system and method for determining an item that has confirmed characteristics are described herein. An image that depicts an object is received from a client device. Structured data that corresponds to characteristics of one or more items are retrieved. A set of characteristics is determined, the set of characteristics being predicted to match with the object. An interface that includes a request for confirmation of the set of characteristics is generated. The interface is displayed on the client device. Confirmation that at least one characteristic from the set of characteristics matches with the object depicted in the image is received from the client device.

PROCESSING APPARATUS

There is provided with a processing apparatus. A data holder holds at least some of data of a plurality of channels in a target layer among a plurality of layers. Each of a plurality of processors performs, in parallel, a product-sum operation using the data of one channel of the target layer and a coefficient corresponding to the target layer. A selector selects whether to perform first processing or second processing on the basis of information specifying processing in the target layer. The first processing includes inputting the data of one channel of the target layer into one of the plurality of processors. The second processing includes inputting the data of one channel of the target layer to the plurality of processors in parallel. 1. A processing apparatus for performing operations with a convolutional neural network having a plurality of layers , the apparatus comprising:a data holder configured to hold at least some of data of a plurality of channels in a target layer among the plurality of layers;a plurality of processors, each configured to perform, in parallel, a product-sum operation using the data of one channel of the target layer and a coefficient corresponding to the target layer; anda selector configured to select whether to perform first processing or second processing on the basis of information specifying processing in the target layer, the first processing including inputting the data of one channel of the target layer, held by the data holder, into one of the plurality of processors, and the second processing including inputting the data of one channel of the target laver, held by the data holder, to the plurality of processors in parallel.2. The processing apparatus according to claim 1 ,wherein in the second processing, the data of each one channel among the plurality of channels in the target layer is input to the plurality of processors from the data holder in parallel.3. The processing apparatus according to claim 2 ,wherein in the second ...

METHOD AND APPARATUS FOR DETECTING DOOR IMAGE BY USING MACHINE LEARNING ALGORITHM

Provided are a method and an apparatus for detecting door image using machine learning algorithm that can easily detect a door image from a design drawing. The method for detecting a door image using a machine learning algorithm includes extracting a plurality of element images from the drawing; filtering the extracted element images using at least one of the size of the image and the number of right angle components; obtaining histogram information projected on the basis of a specific axis with respect to each of the filtered element images; and detecting at least one door image of the filtered element images by using the obtained histogram information as a feature information. 1. A method for detecting a door image using a machine learning algorithm , which extracts the door image from a drawing including a plurality of element images , the method comprising:extracting a plurality of element images from the drawing;filtering the extracted element images using at least one of the size of the image and the number of right angle components;obtaining histogram information projected on the basis of a specific axis with respect to each of the filtered element images; anddetecting at least one door image of the filtered element images by using the obtained histogram information as a feature information.2. The method for detecting a door image using a machine learning algorithm according to claim 1 , further comprising: performing machine learning by using the obtained histogram information projected as the feature information.3. The method for detecting door image using machine learning algorithm according to claim 1 , wherein the extracting step comprising:binarizing the drawing image; andextracting a plurality of element images distinguished from each other in the binarized drawing images.4. The method for detecting door image using machine learning algorithm according to claim 1 , wherein the filtering step comprises a step of excluding the element images having an ...

ARTIFICIAL INTELLIGENCE PROCESSOR AND METHOD OF PERFORMING NEURAL NETWORK OPERATION THEREOF

An artificial intelligence (AI) processor includes at least one memory; a plurality of neural network operators comprising circuitry configured to process an image; and a controller configured to control the at least one memory and the plurality of neural network operators. The controller controls input image data of an image to be stored in the at least one memory and controls at least one of the plurality of neural network operators to perform a neural network operation on image data split based on a size of the image and data processing capabilities of the plurality of neural network operators, and output upscaled image data. 1. An artificial intelligence (AI) processor comprising:at least one memory;a plurality of neural network operators comprising circuitry configured to process an image; anda controller configured to control the at least one memory and the plurality of neural network operators,wherein the controller is further configured to control input image data of an image to be stored in the at least one memory;control at least one of the plurality of neural network operators to perform a neural network operation on image data split based on a size of the image and data processing capabilities of the plurality of neural network operators; andoutput upscaled image data.2. The AI processor of claim 1 , wherein the controller is further configured to deactivate neural network operators that do not perform a neural network operation processing on the split image data.3. The AI processor of claim 1 , wherein the controller is further configured to split the image data based on a horizontal size of the image and a unit of data processing of the plurality of neural network operators.4. The AI processor of claim 1 , wherein the at least one memory comprises a plurality of N-line memories respectively corresponding to the plurality of neural network operators claim 1 ,wherein the plurality of N-line memories comprise at least one of an operation value required ...

VIDEO TO DATA

A method and system can generate video content from a video. The method and system can include a coordinator, an image detector, and an object recognizer. The coordinator can be communicatively coupled to a splitter and/or to a plurality of demultiplexer nodes. The splitter can be configured to segment the video. The demultiplexer nodes can be configured to extract audio files from the video and/or to extract still frame images from the video. The image detector can be configured to detect images of objects in the still frame images. The object recognizer can be configured to compare an image of an object to a fractal. The recognizer can be further configured to update the fractal with the image. The coordinator can be configured to embed metadata about the object into the video. 1. A system for generating data from a video , comprising:a coordinator communicatively coupled to a splitter and to a plurality of demultiplexer nodes, wherein the splitter is configured to segment the video, wherein the demultiplexer nodes are configured to extract audio files from the video and to extract still frame images from the video;an image detector configured to detect images of objects in the still frame images;an object recognizer configured to compare an image of an object to a fractal, wherein the recognizer is further configured to update the fractal with the image; andwherein the coordinator is configured to embed metadata about the object into the video.2. The system of claim 1 , wherein the metadata comprises a timestamp and a coordinate location of the object in one or more of the still frame images.3. The system of claim 1 , wherein the coordinator is configured to create additional demultiplexer processing capacity.4. The system of claim 3 , wherein the coordinator is configured to create additional demultiplexer nodes when the demultiplexer nodes reach at least 80% of processing capacity.5. The system of claim 1 , wherein the demultiplexer nodes generate a confidence ...

VIDEO MONITORING METHOD AND VIDEO MONITORING DEVICE

This application provides a video monitoring method and device. The video monitoring method includes: obtaining video data; inputting at least one frame in the video data into a first neural network to determine object amount information of each pixel dot in the at least one frame; and executing at least one of the following operations by using a second neural network: performing a smoothing operation based on the object amount information in the at least one frame to rectify the object amount information; determining object density information of each pixel dot in the at least one frame based on scene information and the object amount information; predicting object density information of each pixel dot in a to-be-predicted frame next to the at least one frame based on the scene information, the object amount information, and association information between the at least one frame and the to-be-predicted frame. 1. A video monitoring method , comprising:obtaining video data acquired by a video data acquiring module;inputting at least one frame in the video data into a first neural network that is trained in advance, so as to determine object amount information of each pixel dot in the at least one frame; and performing a smoothing operation based on the object amount information in the at least one frame so as to rectify the object amount information;', 'determining object density information of each pixel dot in the at least one frame based on scene information of the acquisition scene for the video data and the object amount information in the at least one frame;', 'predicting object density information of each pixel dot in a to-be-predicted frame next to the at least one frame based on the scene information of the acquisition scene for the video data, the object amount information in the at least one frame, and association information between the at least one frame and the to-be-predicted frame., 'executing at least one of the following operations by using a second ...

INFERENCE DEVICE, CONVOLUTION COMPUTATION METHOD AND PROGRAM

An inference device comprises a weight storage part that stores weights, an input data storage part that stores input data, and a PE (Processing Element) that executes convolution computation in convolutional neural network using the weights and input data. The PE adds up weight elements to be multiplied with elements of the input data for each of variable values of the elements of the input data. The PE multiplies each of the variable values of the elements of the input data with each cumulative sum value of weights corresponding to the variable values of the elements of the input data. The PE adds up a plurality of multiplication results obtained by the multiplications. 1. An inference device , comprising:a weight storage part that stores weights,an input data storage part that stores input data, anda PE (Processing Element) that executes convolution computation in convolutional neural network using the weights and input data, whereinthe PE is configured to:add up weight elements to be multiplied with elements of the input data for each of variable values of the elements of the input data,multiply each of the variable values of the elements of the input data with each cumulative sum value of weights corresponding to the variable values of the elements of the input data, andadd up a plurality of multiplication results obtained by the multiplication.2. The inference device according to claim 1 , wherein the PE comprises:a selector into which the elements of the input data and the weight elements are input, anda plurality of accumulation processors each calculating and storing cumulative sum of the weights for each of the variable values of the input data elements,wherein the selector determines an accumulation processor to be an output destination of the input weight elements among the plurality of accumulation processors.3. The inference device according to claim 2 , whereinthe PE comprises a plurality of multipliers respectively associated with the plurality of ...

Active View Planning By Deep Learning

The disclosure includes a system and method for identifying an object and a viewpoint from an image with a probability that satisfies a predefined criterion based on deep network learning. An active view planning application receives a first image, performs recognition on the first image to determine an object, a viewpoint and a probability of recognition, determines a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken, and identifies a next action from the first action and the second action based on an increase in expected gains. 1. A computer-implemented method comprising:receiving, by a computing device, a first image;performing, by the computing device, recognition with a deep neural network on the first image to determine an object, a viewpoint and a probability of recognition;determining, by the computing device, a first expected gain in the probability of recognition when a first action is taken and a second expected gain in the probability of recognition when a second action is taken; andidentifying a next action from the first action and the second action based on an increase in expected gains.2. The computer-implemented method of claim 1 , further comprising performing the next action.3. The computer-implemented method of claim 1 , further comprising:determining whether the probability of recognition exceeds a predetermined threshold; andresponsive to determining that the probability of recognition does not exceed the predetermined threshold, performing the next action.4. The computer-implemented method of claim 1 , wherein the deep neural network is a convolutional neural network.5. The computer-implemented method of claim 1 , wherein the deep neural network determines a class label having an object label and a viewpoint label.6. The computer-implemented method of claim 1 , further comprising:receiving a set of training data including ...

Intelligent multi-scale medical image landmark detection

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Information processing apparatus, method and computer program product

According to an embodiment, an information processing apparatus includes a memory and processing circuitry. The processing circuitry configured to acquire an input image captured by an image-capturing device installed in a specific location. The processing circuitry configured to perform adaptation processing of adapting an estimation model, which is used for detecting positions or the number of objects contained in an image, to the specific location by sequentially selecting a parameter of the estimation model from a lower level toward a higher level, and by modifying the selected parameter in such a manner to reduce an estimation error in the positions or the number of the objects contained in the input image. The processing circuitry configured to acquire a termination condition for the adaptation processing. The processing circuitry configured to terminate the adaptation processing when the termination condition is satisfied.

METHOD AND APPARATUS FOR BUILDING IMAGE MODEL

A method and apparatus for building an image model, where the apparatus generates a target image model that includes layers duplicated from a layers of a reference image model and an additional layer, and trains the additional layer. 1. A method of building an image model , the method comprising:generating a target image model comprising an additional layer and remaining layers that are same as layers of a reference image model and; andtraining the additional layer of the target image model based on the reference image model.2. The method of claim 1 , wherein the generating of the target image model comprises generating the target image model by connecting the additional layer to a layer located on an input side of the remaining layers in the target image model.3. The method of claim 1 , wherein the generating of the target image model comprises initializing the additional layer by assigning a random value to each nodes of the additional layer.4. The method of claim 1 , wherein the training of the additional layer comprises:determining a reference model output from an image with a converted training input, based on the reference image model; andtraining the target image model based on the reference model output.5. The method of claim 4 , wherein the determining of the reference model output comprises:generating a conversion image by converting the training input based on an input layer of the reference image model; andcomputing the reference model output from the conversion image.6. The method of claim 1 , wherein the training of the additional layer comprises training the target image model based on a reference model output and a target model output that are based on the reference image model and the target image model claim 1 , respectively.7. The method of claim 6 , wherein the training of the additional layer comprises:computing an error based on the reference model output and the target model output; andupdating a parameter of at least a portion of the ...

DISPLAY CONTROL SYSTEM AND RECORDING MEDIUM

There is provided a display control system including a plurality of display units, an imaging unit configured to capture a subject, a predictor configured to predict an action of the subject according to a captured image captured by the imaging unit, a guide image generator configured to generate a guide image that guides the subject according to a prediction result from the predictor, and a display controller configured to, on the basis of the prediction result from the predictor, select a display unit capable of displaying an image at a position corresponding to the subject from the plurality of display units, and to control the selected display unit to display the guide image at the position corresponding to the subject. 1. (canceled)2. A system comprising:an action history information acquirer for acquiring information of user motion around a table;a guide image generator for generating one or more images suggesting an action of the user based on the acquired information; anda display controller for controlling display of the one or more images on the table.3. The system according to claim 2 , further comprising a predictor for generating one or more predicted actions of the user according the information of user motion.4. The system according to claim 3 , wherein the guide image generator generates the one or more images based on claim 3 , at least claim 3 , one of the predicted actions.5. The system according to claim 2 , further comprising a learning unit for learning one or more patterns of items placed on the table claim 2 , and wherein the guide image generator generates the one or more images based on claim 2 , at least claim 2 , one of the patterns.6. The system according to claim 5 , wherein the one or more patterns comprises a pattern of dishes.7. The system according to claim 5 , wherein the one or more patterns comprises a pattern of cutlery.8. The system according to claim 2 , further comprising one or more imaging units for generating the ...

PHOTO IMAGE PROVIDING DEVICE AND PHOTO IMAGE PROVIDING METHOD

A photo image providing method includes learning an artificial neural network repeatedly to obtain user preference image quality information corresponding to a candidate photo image selected from a plurality of candidate photo images, and when obtaining a photo image from a camera, adjusting an image quality of the obtained photo image based on the obtained user preference image quality information. 1. A photo image providing method comprising:learning an artificial neural network repeatedly to obtain user preference image quality information corresponding to a candidate photo image selected from a plurality of candidate photo images; andwhen obtaining a photo image from a camera, adjusting an image quality of the obtained photo image based on the obtained user preference image quality information.2. The method of claim 1 , wherein the learning of the artificial neural network repeatedly comprises:obtaining photographic environment information related to the obtained photo image from among a plurality of photographic environment information when obtaining a photo image from the camera;controlling to display a plurality of candidate photo images based on the obtained photographic environment information; andlearning the artificial neural network to obtain user preference image quality information corresponding to at least one candidate photo image selected from the plurality of candidate photo images.3. The method of claim 1 , further comprising:when a first photo image is obtained from the camera, controlling to display a plurality of candidate photo images, the plurality of candidate photo images being preset as a plurality of candidate photo images of photographic environment information related to the first photo image;learning to obtain first user preference image quality information corresponding to a candidate photo image selected from the plurality of preset candidate photo images;when a second photo image is obtained from the camera, controlling to display a ...

INFORMATION SEARCH SYSTEM, INTELLECTUAL PROPERTY INFORMATION SEARCH SYSTEM, INFORMATION SEARCH METHOD, AND INTELLECTUAL PROPERTY INFORMATION SEARCH METHOD

An information search system or an intellectual property information search system that is capable of highly accurate information search is provided. The intellectual property information search system includes a processing unit. First data and first reference analysis data are input to the processing unit. The first data includes first intellectual property information. The first reference analysis data includes plural pieces of second intellectual property information. The processing unit is configured to search the first reference analysis data for data similar to the first data to generate second data. The processing unit is configured to output the second data. The second data includes a piece of the second intellectual property information similar to the first intellectual property information and information showing the degree of similarity of the piece of the second intellectual property information to the first intellectual property information. 1. An information search system comprising a processing unit ,wherein:first data and first reference analysis data are input to the processing unit,the first data includes first information,the first reference analysis data includes plural pieces of second information,the processing unit is configured to search the first reference analysis data for data similar to the first data to generate second data and output the second data, and a piece of the second information similar to the first information; and', 'third information showing a degree of similarity of the piece of the second information to the first information., 'the second data includes2. The information search system according to claim 1 ,wherein the first information is first intellectual property information, andwherein the plural pieces of the second information are plural pieces of second intellectual property information.3. The information search system according to claim 1 ,wherein:the first data includes text data,the first reference analysis data ...

Image Retrieval with Deep Local Feature Descriptors and Attention-Based Keypoint Descriptors

Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application. 1. A computer-implemented method of image retrieval , comprising:receiving, by a computing system comprising one or more computing devices, a query image;determining, by the computing system, a plurality of local feature descriptors from the query image;determining, by the computing system, an attention score for each local feature descriptor;determining, by the computing system, a set of keypoint descriptors for the query image based at least in part on the attention scores, the set of keypoint descriptors corresponding to a subset of the local feature descriptors;reducing, by the computing system, a spatial dimensionality of the set of keypoint descriptors for the query image; andretrieving, by the computing system, one or more images corresponding to the query image, based at least in part on the set of keypoint descriptors for the query image.2. The computer-implemented method of image retrieval of claim 1 , wherein the set of keypoint descriptors comprises a predetermined number of local feature descriptors having the highest ...

FAST ITEM IDENTIFICATION FOR CHECKOUT COUNTER

Methods, systems, and computer programs are presented for on-the-fly image recognition of an item in motion. One method includes an operation for periodically capturing images, by several cameras, of a recognition area. Further, the method includes detecting that the item is present in the recognition area and selecting a recognition window that defines a period of time for analysis. The recognition window defines recognition frames corresponding to the images captured within the recognition window. Each recognition frame is analyzed to determine if an identity of the item has been obtained for the recognition frame, the analysis being based on image recognition of the recognition frame to identify the item based on, at least, a shape of the item and coloring of the item. Further, the method includes operations for determining if the item has been identified based on the analysis for the recognition frames, and for displaying the item identification. 1. A method comprising:periodically capturing images, by a plurality of cameras, of a recognition area defined for identifying an item placed in the recognition area while the item is held by a user;detecting, by one or more processors, that the item is present in the recognition area based on the captured images;selecting, by the one or more processors, a recognition window that defines a period of time for analyzing the item, the recognition window defining a plurality of recognition frames corresponding to the images that have been captured within the recognition window;analyzing, by the one or more processors, each recognition frame to determine if an identity of the item has been obtained for the recognition frame, the analyzing comprising performing image recognition of the recognition frame to identify the item based on, at least, a shape of the item and coloring of the item;determining, by the one or more processors, if the item has been identified based on the analysis for the plurality of recognition frames; ...

METHODS, APPARATUS AND ARTICLES OF MANUFACTURE TO USE BIOMETRIC SENSORS TO CONTROL AN ORIENTATION OF A DISPLAY

Methods, systems and articles of manufacture for a portable electronic device to change an orientation in which content is displayed on a display device of the portable electronic device based on a facial image. Example portable electronic devices include a display device, an image sensor to capture a facial image of a user of the portable electronic device, an orientation determination tool to determine a device orientation relative to the user based on the facial image of the user, and an orientation adjustment tool. The orientation adjustment tool changes a content orientation in which the display device of the portable electronic device presents content based on the determination of the device orientation. 1. A portable electronic device comprising:a display device;an image sensor to capture a facial image of a user of the portable electronic device;an orientation determination tool to determine a device orientation relative to the user based on the facial image of the user; andan orientation adjustment tool to change a content orientation in which the display device of the portable electronic device presents content based on the determination of the device orientation.2. The portable electronic device of claim 1 , wherein the image sensor is to capture the facial image of the user when the portable electronic device is in a locked mode and the content includes a request for entry of user authentication information.3. The portable electronic device of claim 2 , further including a motion sensor claim 2 , the motion sensor to send a notification to the image sensor when motion is sensed claim 2 , the image sensor to capture the facial image in response to the notification.4. The portable electronic device of claim 1 , wherein the image sensor is to capture the facial image of the user when the portable electronic device is in a locked mode claim 1 , and the orientation adjustment tool is to use the facial image to determine whether to unlock the portable ...

TEMPERATURE COMPENSATION FOR STRUCTURED LIGHT DEPTH IMAGING SYSTEM

Disclosed are an apparatus and a method of compensating temperature shifts of a structured light pattern for a depth imaging system. In some embodiments, a depth imaging device includes a light source, an imaging sensor and a processor. The light source emits light corresponding to a pattern. A temperature drift of the light source can cause a shift of the pattern. The imaging sensor receives the light reflected by environment in front of the depth imaging device and generates a depth map including a plurality of pixel values corresponding to depths of the environment relative to the depth imaging device. The processor estimates the shift of the pattern based on a polynomial model depending on the temperature drift of the light source. The processor further adjusts the depth map based on the shift of the pattern. 1. A depth imaging device , comprising: receive light as reflected by an environment of the depth imaging device;', 'generate, based on the light, a depth map including a plurality of pixel values corresponding to depths of the environment relative to the depth imaging device;, 'an imaging sensor configured toa temperature sensor configured to measure a temperature drift from a reference temperature of one or more of a light source, an optical component of the depth imaging device, or the environment of the depth imaging device; and estimate a shift of a pattern of the light based on the temperature drift; and', 'adjust the depth map based on the shift of the pattern., 'a processor configured to2. The depth imaging device of claim 1 , wherein the pattern is a speckle pattern corresponding to a reference image including a plurality of dots claim 1 , each of the dots of the plurality of dots having known coordinates in the reference image.3. The depth imaging device of claim 1 , wherein the shift of the pattern is estimated by using a polynomial model depending on the temperature drift.4. The depth imaging device of claim 3 , wherein the polynomial model is ...

SYSTEM FOR DETERMINING ANATOMICAL FEATURE ORIENTATION

The systems and methods disclosed herein provide determination of an orientation of a feature towards a reference target. As a non-limiting example, a system consistent with the present disclosure may include a processor, a memory, and a single camera affixed to the ceiling of a room occupied by a person. The system may analyze images from the camera to identify any objects in the room and their locations. Once the system has identified an object and its location, the system may prompt the person to look directly at the object. The camera may then record an image of the user looking at the object. The processor may analyze the image to determine the location of the user's head and, combined with the known location of the object and the known location of the camera, determine the direction that the user is facing. This direction may be treated as a reference value, or “ground truth.” The captured image may be associated with the direction, and the combination may be used as training input into an application. 1. An apparatus for determining an orientation of an anatomical feature , comprising: identify an anatomical feature of a subject within an environment; and', 'determine a location of the anatomical feature of the subject within the environment; and, 'anatomical feature location determination logic to prompt the subject to orient the anatomical feature toward a target within the environment;', 'determine a location of the target within the environment; and', 'determine an orientation of the anatomical feature based on the location of the anatomical feature and the location of the target., 'orientation reference logic to2. The apparatus of claim 1 , wherein the orientation reference logic is further to cause a camera to capture an image of the anatomical feature of the subject oriented toward the location of the target.3. The apparatus of claim 2 , wherein the orientation reference logic is further to:determine metadata based upon the determined orientation of ...

METHOD AND SYSTEM FOR DETERMINIG TREATMENTS BY MODIFYING PATIENT-SPECIFIC GEOMETRICAL MODELS

Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options. 128-. (canceled)29. A method for planning treatment for arterial stenotic lesions by processing patient-specific images of a patient , comprising:identifying a set of stenotic lesions in a patient's coronary arteries from medical image data of the patient;generating a plurality of treatment options for the set of stenotic lesions, wherein each of the plurality of treatment options corresponds to a stenting configuration in which one or more of the stenotic lesions are stented;calculating, for each of the plurality of treatment options, predicted hemodynamic metrics for the set of stenotic lesions resulting from the stenting configuration corresponding to that treatment option; anddetermining an optimum treatment option from the plurality of treatment options based on the predicted hemodynamic metrics for the set of stenotic lesions calculated for each of the plurality of treatment options, and based on a number of stents in the stenting configuration corresponding to each of the plurality of treatment options.30. The ...

METHOD FOR MITIGATING DISEASE TRANSMISSION IN A FACILITY

A method for mitigating disease transmission in a facility includes: accessing a set of extant disease metrics associated with a reporting period; accessing a set of images of the facility captured during the reporting period by a set of sensor blocks deployed in the facility; aggregating the set of images into a timeseries of facility maps depicting the facility during the reporting period; identifying a set of objects in the timeseries of facility maps, the set of objects comprising a set of humans; generating a transmission feature vector based on the set of objects in the timeseries of facility maps and the set of extant disease metrics associated with the reporting period; calculating a predicted timeseries of health metrics for the facility based on the transmission feature vector and a facility health model; and prompting a mitigation response at the facility based on the predicted timeseries of health metrics. 1. A method for mitigating disease transmission in a facility comprising: [ accessing a training set of extant disease metrics associated with the sampling period;', 'accessing a training set of images of the facility captured during the sampling period by a set of sensor blocks deployed in the facility;', 'aggregating the training set of images into a training timeseries of facility maps depicting the facility during the sampling period;', 'identifying a training set of objects in the timeseries of facility maps, the training set of objects comprising a training set of humans;', 'generating a training transmission feature vector based on the training set of objects in the training timeseries of facility maps and the training set of extant disease metrics associated with the sampling period, the training transmission feature vector; and', 'accessing a timeseries of health metrics for a causal period subsequent to the sampling period; and, 'for each sampling period in the training period, the training transmission feature vector for each sampling period ...

LATENT EMBEDDINGS FOR WORD IMAGES AND THEIR SEMANTICS

A system and method enable semantic comparisons to be made between word images and concepts. Training word images and their concept labels are used to learn parameters of a neural network for embedding word images and concepts in a semantic subspace in which comparisons can be made between word images and concepts without the need for transcribing the text content of the word image. The training of the neural network aims to minimize a ranking loss over the training set where non relevant concepts for an image which are ranked more highly than relevant ones penalize the ranking loss. 1. A semantic comparison method , comprising:providing training word images labeled with concepts;with the training word images and their labels, learning a first embedding function for embedding word images in a semantic subspace into which the concepts are embedded with a second embedding function;receiving a query comprising at least one test word image or at least one concept;where the query comprises at least one test word image, generating a representation of each of the at least one test word image, comprising embedding the test word image in the semantic subspace with the first embedding function;where the query comprises at least one concept, providing a representation of the at least one concept generated by embedding each of the at least one concept the embedding function; a) at least one of the test word image representations, and', at least one of the concept representations, and', 'another of test word image representations; and, 'b) at least one of], 'computing a comparison betweenoutputting information based on the comparison.2. The method of claim 1 , wherein at least one of the learning of the first embedding function claim 1 , generating of the representation of each of the at least one test word image claim 1 , providing a representation of the at least one concept claim 1 , and computing of the comparison is performed with a processor.3. The method of claim 1 , ...

EXTRACTING GRADIENT FEATURES FROM NEURAL NETWORKS

A method for extracting a representation from an image includes inputting an image to a pre-trained neural network. The gradient of a loss function is computed with respect to parameters of the neural network, for the image. A gradient representation is extracted for the image based on the computed gradients, which can be used, for example, for classification or retrieval. 1. A method for extracting a representation from an image , comprising:inputting an image to a pre-trained neural network;computing a gradient of a loss function with respect to parameters of the neural network for the image; andextracting a gradient representation of the image based on the computed gradients,wherein at least one of the computing and the extracting is performed with a processor.2. The method of claim 1 , wherein the computing of the gradient of the loss function comprises:computing and extracting a set of forward features from the neural network;computing and extracting a set of backward features from the neural network, the backward features comprising a gradient of the loss with respect to the output of a selected layer of the neural network computed in a backward pass of the neural network; andcombining the forward and the backward features to construct a set of gradient features, the gradient features comprising a gradient of the loss with respect to the parameters of a selected layer of the neural network.3. The method of claim 1 , wherein the forward and backward features are combined by matrix multiplication.4. The method of claim 1 , wherein in a forward pass of the neural network claim 1 , a prediction vector of values is output for the image which includes a prediction value for each of a set of classes claim 1 , the computing the gradient of a loss function comprising computing a vector of error values based on a difference between the prediction vector and a standard vector comprising a standard value for each of the classes claim 1 , and backpropagating the error ...

FINDING SEMANTIC PARTS IN IMAGES

Embodiments of the present invention relate to finding semantic parts in images. In implementation, a convolutional neural network (CNN) is applied to a set of images to extract features for each image. Each feature is defined by a feature vector that enables a subset of the set of images to be clustered in accordance with a similarity between feature vectors. Normalized cuts may be utilized to help preserve pose within each cluster. The images in the cluster are aligned and part proposals are generated by sampling various regions in various sizes across the aligned images. To determine which part proposal corresponds to a semantic part, a classifier is trained for each part proposal and semantic part to determine which part proposal best fits the correlation pattern given by the true semantic part. In this way, semantic parts in images can be identified without any previous part annotations. 1. One or more computer storage media storing computer-useable instructions that , when used by a computing device , cause the computing device to perform a method for finding semantic parts in images , the method comprising:applying a convolutional neural network (CNN) to a set of images, the CNN detecting features for each image, each image being defined by a feature vector;clustering a subset of the set of images in accordance with a similarity between feature vectors;generating a plurality of part proposals, the plurality of part proposals comprising parts at various locations and of various sizes for an image of the subset of images; andassociating, via information gain matching, a label with at least one of the parts for the image.2. The one or more computer storage media of claim 1 , wherein the features are detected by the fourth layer of the CNN.3. The one or more computer storage media of claim 2 , further comprising utilizing Euclidean distance as a distance metric between feature vectors after Lnormalization.4. The one or more computer storage media of claim 1 , ...

COMPUTERIZED, PERSONAL-COLOR ANALYSIS SYTEM

A method and apparatus to automatically generate a customized color palette receives data corresponding to several physical attributes of a living subject, such as hair, skin and eyes and conducts an analysis uniquely corresponding to each attribute, and may associate each attribute with at least one property such as a color family, a technical identifier, an intensifier, a temperature, a power color, and a saturation designation. Software may identify the color linked to each attribute by, for example, an LAB value, an RGB value, an HTML value, an XYZ value or a CMY value, while an intensifier may be a contrasting color, and saturation may indicate whether the color linked to the attribute is a tone, tint, shade or hue. A color palette uniquely compatible with the subject may be presented by print media, monitor, printer, cell phone, personal digital assistant, or the like. 1. At a computer system , the computer system including a processor , a method for recommending a customized color palette for a human subject , the method comprising:mapping a first general color acquired for the human subject to a corresponding first general color value in a multi-category color scheme, the multi-category color scheme representing ranges of adjacent color categories using values ranging from lower values for colors in a light category through middle values for colors in a middle category to higher values for colors in vivid category, the first general color corresponding to a physical feature of the human subject;mapping a second general color acquired for the human subject to a corresponding second general color value in the multi-category color scheme, the second general color corresponding to another different physical feature of the human subject; when the first general color value and the second general color value are identified as being included within the same color category or within two adjacent color categories of the multi-category color scheme, generating an ...

Computer Vision Based Driver Assistance Devices, Systems, Methods and Associated Computer Executable Code

The present invention includes computer vision based driver assistance devices, systems, methods and associated computer executable code (hereinafter collectively referred to as: “ADAS”). According to some embodiments, an ADAS may include one or more fixed image/video sensors and one or more adjustable or otherwise movable image/video sensors, characterized by different dimensions of fields of view. According to some embodiments of the present invention, an ADAS may include improved image processing. According to some embodiments, an ADAS may also include one or more sensors adapted to monitor/sense an interior of the vehicle and/or the persons within. An ADAS may include one or more sensors adapted to detect parameters relating to the driver of the vehicle and processing circuitry adapted to assess mental conditions/alertness of the driver and directions of driver gaze. These may be used to modify ADAS operation/thresholds. 1. A system for computer vision based driver assistance , said system comprising:an adjustable camera having an angle of view of 75 degrees or greater and adapted to be adjustably mounted to a vehicle, such that the orientation of said camera in relation to the vehicle is adjustable;one or more fixed cameras, having an angle of view of 70 degrees or less and adapted to be mounted to a vehicle, such that the orientation of said camera in relation to the vehicle is fixed;first processing circuitry communicatively coupled to said adjustable and fixed cameras and adapted to process images captured by said adjustable and fixed cameras to identify hazardous situations relating to the vehicle.2. The system according to claim 1 , wherein said one or more fixed cameras comprise at least two cameras having an angle of view of 70 degrees or less.3. The system according to claim 2 , wherein said at least two cameras capture stereo images of an area in front of the vehicle and said first processing circuitry is further adapted to derive depth information ...

IMAGE CLASSIFICATION METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM

An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class. 1. An image classification method , comprising:inputting a to-be-classified image into a plurality of neural network models;obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models;respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class;obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; anddetermining, according to each obtained probability, whether the to-be-classified image comprises the object image of the preset class.2. The method according to claim 1 , wherein generating the plurality of corresponding image features further comprises:obtaining vectors outputted by the multiple non-input layers specified among one or more intermediate layer and an output layer of each neural network model; ...

Generating and utilizing normalized scores for classifying digital objects

The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.

MACHINE LEARNING IMAGE PROCESSING

A machine learning image processing system performs natural language processing (NLP) and auto-tagging for an image matching process. The system facilitates an interactive process, e.g., through a mobile application, to obtain an image and supplemental user input from a user to execute an image search. The supplemental user input may be provided from a user as speech or text, and NLP is performed on the supplemental user input to determine user intent and additional search attributes for the image search. Using the user intent and the additional search attributes, the system performs image matching on stored images that are tagged with attributes through an auto-tagging process. 1. A machine learning image processing system comprising:a data repository storing images and tags for each image, wherein the tags for each image describe attributes of an object in the image;a network interface to connect the machine learning image processing system to at least one network;at least one processor to execute machine readable instructions stored on at least one non-transitory computer readable medium; 'wherein the plurality of image attribute machine learning classifiers comprise convolutional neural networks trained to identify the attributes;', 'at least one data storage to store a plurality of image attribute machine learning classifiers,'} apply each image stored in the data repository to the plurality of image attribute machine learning classifiers;', 'determine predictions for a plurality of image attribute categories from outputs of the plurality of image attribute machine learning classifiers;', 'determine the attributes of the object in each image stored in the data repository from the predictions; and', 'tag each image stored in the data repository with the determined attributes for the object in the image., 'wherein the machine readable instructions comprise machine readable instructions for an auto-tagging subsystem, and the at least one processor is to execute ...

INFORMATION SEARCH SYSTEM, INTELLECTUAL PROPERTY INFORMATION SEARCH SYSTEM, INFORMATION SEARCH METHOD, AND INTELLECTUAL PROPERTY INFORMATION SEARCH METHOD

An information search system or an intellectual property information search system that is capable of highly accurate information search is provided. The intellectual property information search system includes a processing unit. First data and first reference analysis data are input to the processing unit. The first data includes first intellectual property information. The first reference analysis data includes plural pieces of second intellectual property information. The processing unit is configured to search the first reference analysis data for data similar to the first data to generate second data. The processing unit is configured to output the second data. The second data includes a piece of the second intellectual property information similar to the first intellectual property information and information showing the degree of similarity of the piece of the second intellectual property information to the first intellectual property information. 1. A system in which a text data is supplied , the text data is subjected to a morphological analysis , and a neural network process is performed by using a result of the morphological analysis , thereby searching a similar data to the text data from a reference text data.2. A system in which first intellectual property information including a text data is supplied , the text data is subjected to a morphological analysis , and a neural network process is performed by using a result of the morphological analysis , thereby searching a similar data to the text data from a reference text data , and generating an analysis data including second intellectual property information.3. The system according to claim 2 , wherein the analysis data includes information showing a degree of similarity of the second intellectual property information to the first intellectual property information.4. A system in which a text data and date information are supplied claim 2 , the text data is subjected to a morphological analysis claim 2 ...

FACE RECOGNITION IN A RESIDENTIAL ENVIRONMENT

A face recognition system of a residential environment identifies an individual present in the residential environment. The residential environment include a plurality of home devices and is associated with a group of different persons. The face recognition system identifies which person in the group is the individual and generate an operating instruction for a home device based on identity of the individual. For example, the face recognition system captures an image set of the individual's head and face and applies the image set to a machine learning model that is trained to distinguish between the different persons based on images of their heads and faces. The face recognition system can retrieve a personal profile of the identified individual, which includes settings of the home device for the identified individual. The face recognition system generates the operating instruction based on the personal profile. 1. In a residential environment comprising a plurality of home devices , a method for identifying an individual present in the residential environment , the method comprising:capturing a set of one or more images of an individual's head and face while the individual is present in the residential environment;applying the image set as an input to a machine learning model that has been trained to distinguish between a group of different persons associated with the residential environment based on images of the persons' heads and faces;the machine learning model identifying which person in the group is the individual; andgenerating an operating instruction for one of the home devices based on the identity of the individual.2. The method of wherein the machine learning model can further identify that none of the persons in the group is the individual.3. The method of wherein the machine learning model has been trained on reference images of the different persons' heads and faces.4. The method of wherein the reference images include different views of the persons' ...

SURVEILLANCE SYSTEM AND METHOD FOR PREDICTING PATIENT FALLS USING MOTION FEATURE PATTERNS

A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison. 1. A surveillance system for detecting a fall risk condition , the system comprising:a storage device configured to store motion feature patterns that are extracted from video recordings of patient activities, the motion feature patterns each being representative of motion associated with a risk event or a non-risk event based on a centroid of motion corresponding to movement proximate to a fall risk area; and a classifier component that generates a fall alert by analyzing a plurality of frames generated by a surveillance camera, and', determine a centroid of motion of one or more frames from the plurality of frames that correspond to the fall alert,', 'compare the centroid of motion of the one or more frames with the motion feature patterns, and', 'determine whether to confirm the fall alert based on the comparison., 'an alarm verifier configured to], 'at least one server comprising2. The system of wherein the alarm verifier is configured to identify real alarm cases or false-alarm cases of fall events based on accelerometer information worn by a patient corresponding to the video recordings.3. The system of wherein the alarm verifier is configured to determine ...

CLASSIFICATION BASED ON ANNOTATION INFORMATION

Systems and techniques for classification based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The system also generates a first loss function based on the plurality of masks, a second loss function based on a plurality of image level labels associated with the plurality of images, and a third loss function based on the first loss function and the second loss function, where the third loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network. 1. A machine learning system , comprising:a memory that stores computer executable components; a training component that trains a convolutional neural network based on training data and a plurality of images, wherein the training data is associated with a plurality of patients from at least one imaging device, and wherein the plurality of images is associated with a plurality of masks from a plurality of objects;', 'a first loss function component that generates a first loss function based on the plurality of masks;', 'a second loss function component that generates a second loss function based on a plurality of image level labels associated with the plurality of images;', 'a third loss function component that generates a third loss function based on the first loss function and the second loss function, wherein the third loss function is iteratively back propagated to tune parameters of the convolutional neural network; and', 'a classification component that predicts a classification label for an input image based on the convolutional neural network., 'a processor that executes computer ...

CLASSIFICATION BASED ON ANNOTATION INFORMATION

Systems and techniques for classification based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The plurality of images is associated with a plurality of masks, a plurality of image level labels, and/or a bounding box. The system also generates a first loss function based on the plurality of masks, a second loss function based on the plurality of image level labels, and a third loss function based on the bounding box. Furthermore, the system generates a fourth loss function based on the first loss function, the second loss function and the third loss function, where the fourth loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network. 1. A machine learning system , comprising:a memory that stores computer executable components; a training component that trains a convolutional neural network based on training data and a plurality of images, wherein the training data is associated with a plurality of patients from at least one imaging device, and wherein the plurality of images is associated with a plurality of masks from a plurality of objects, or a plurality of image level labels for the plurality of images, or a bounding box that links a region of interest to a class label;', 'a first loss function component that generates a first loss function based on the plurality of masks;', 'a second loss function component that generates a second loss function based on the plurality of image level labels for the plurality of images;', 'a third loss function component that generates a third loss function based on the bounding box that links a region of interest to the class label;', 'a fourth loss function component that generates a fourth loss function based on the first loss function, the second loss function and the ...

COMPOSITIONS AND METHODS FOR TREATING CUTANEOUS CONDITIONS

A composition for treating cutaneous condition is provided and has 0.001-1.000 wt. % Leaf Juice, 1.000-40.000 wt. % Cyclopentasiloxane and Dimethicone Crosspolymer, 0.50-15.000 wt. % Dimethicone, 1.00-10.000 wt. % Ethoxydiglycol, 0.100-5.000 wt. % Glycerin, Water and Extract Mixture, 1.000-30.000 wt. % Glycerin, 0.500-5.000 wt. % Hydroxyethyl Acetate/Sodium Acryloyldimethy Taurate Copolymer, 0.600-1.100 wt. % Phenoxyyethanoland Ethylexylglycerin, 1-6.00 wt. fry PPG-12 SMDI Copolymer, 0.00100-0.1000 wt. % GMP Grade Recombinant Human TGF-B3, 0.010-0.3000 wt. % GMP Grade Recombinant Human IL10, 0.010-0.9000 wt. % GMP Grade Recombinant Human bFGF, 0.010-2.00 wt, % Sodium Hyaluronate, 0.1000-10.000 wt. % Tetrahexyldecyl Ascorbate, 30.000-80.000 wt % Water. 1Aloe BarbadensisCentella asiatica. A composition for treating cutaneous condition which comprises 0.015-0.250 wt. % Leaf Juice , 5.000-15.000 wt. % Cyclopentasiloxane and Dimethicone Crosspolymer , 4.00-8.000 wt. % Dimethicone , 1.50-4.000 wt. % Ethoxydiglycol , 1.000-3.000 wt. % Glycerin , Water and Extract Mixture , 12.000-25.000 wt. % Glycerin , 2.000-4.000 wt. % Hydroxyethyl Acetate/Sodium Acryloyldimethy Taurate Copolymer , 0.800-1.100 wt. % Phenoxyyethanol and Ethylexylglycerin , 1.500-4.00 wt. % PPG-12 SMDI Copolymer , 0.0010-0.1000 wt. % GMP Grade Recombinant Human TGF-B3 , 0.010-0.3000 wt. % GMP Grade Recombinant Human IL10 , 0.010-0.9000 wt. % GMP Grade Recombinant Human bFGF , 0.25-1.00 wt. % Sodium Hyaluronate , 1.000-3.000 wt. % Tetrahexyldecyl Ascorbate , and water to make 100%2Aloe BarbadensisCentella asiatica. A composition for treating cutaneous conditions which comprises 0.001-1.000 wt. % Leaf Juice , 1.000-40.000 wt. % Cyclopentasiloxane and Dimethicone Crosspolymer , 0.50-15.000 wt. % Dimethicone , 1.00-10.000 wt. % Ethoxydiglycol , 0.100-5.000 wt. % Glycerin , Water and Extract Mixture , 1.000-30.000 wt. % Glycerin , 0.500-5.000 wt. % Hydroxyethyl Acetate/Sodium Acryloyldimethy Taurate Copolymer , ...

COMPUTER-IMPLEMENTED METHODS, COMPUTER-READABLE MEDIA AND ELECTRONIC DEVICES FOR VIRTUAL CONTROL OF AGRICULTURAL DEVICES

A method for controlling a plurality of mobile agricultural devices that includes establishing electronic communication with a plurality of transceivers mounted to the mobile agricultural devices. The method also includes building a three-dimensional model including a virtual representation of each of the mobile agricultural devices and displaying the three-dimensional model at a user interface having a display. The method further includes receiving location data regarding the mobile agricultural devices via the transceivers and adjusting at least one of the virtual representations of the mobile agricultural devices within the model to reflect the location data. The method still further includes receiving, via the user interface, a user input comprising a command relating to operation of a first one of the mobile agricultural devices and transmitting the user input command to one of the transceivers, which is mounted to the first mobile agricultural device, so as to implement a change in operation of the first mobile agricultural device. 1. A method for controlling a plurality of mobile agricultural devices implemented via one or more transceivers and one or more processors , the method comprising:establishing electronic communication with a plurality of transceivers mounted to respective ones of the plurality of mobile agricultural devices;building a three-dimensional model including a virtual representation of each of the plurality of mobile agricultural devices;displaying the three-dimensional model at a user interface including a display;receiving location data regarding the plurality of mobile agricultural devices via the plurality of transceivers;adjusting at least one of the virtual representations of the plurality of mobile mechanical devices within the model to reflect the location data;receiving, via the user interface, a user input comprising a command relating to operation of a first mobile agricultural device of the plurality of mobile agricultural ...

DEEP PRODUCT PLACEMENT

One embodiment provides a method comprising identifying a product placement opportunity for a product in a frame of a piece of content during playback of the piece of content on a display device. The method further comprises determining a location in the frame to insert product placement content for the product based on a learned statistical model representing learned placement patterns related to the product. The method further comprises modifying the product placement content based on one or more objects present in the frame, and inserting a product placement for the product in the piece of content by inserting the modified product placement content in the frame based on the location. The modified product placement content appears to occur naturally in the piece of content. 1. A method comprising:identifying a product placement opportunity for a product in a frame of a piece of content during playback of the piece of content on a display device;determining a location in the frame to place product placement content associated with the product based on a learned statistical model representing learned placement patterns related to the product; andplacing the product placement content in the frame based on the location, wherein the product placement content appears to occur naturally in the piece of content.2. The method of claim 1 , further comprising:modifying the product placement content based on information related to the frame.3. The method of claim 2 , further comprising:receiving a product placement profile, wherein the product placement profile includes the product placement content and one or more placement instructions for placing the product placement content in media content.4. The method of claim 3 , wherein identifying a product placement opportunity for a product in a frame comprises:applying a learned object detector model to the frame to detect presence of the product and one or more objects that fit the product placement profile.5. The method of ...

BIOMETRIC RECOGNITION APPARATUS AND BIOMETRIC RECOGNITION METHOD

A biometric recognition apparatus and fingerprint feature extraction method can automatically optimize parameters used for extracting a feature template from a biometric image. The biometric recognition apparatus includes: a teacher data generation unit that generates a genuine pair and an imposter pair of first and second biometric images; a learning data generation unit that uses a plurality of different temporary parameters to extract feature templates from the first biometric image and the second biometric image; and an optimum solution determination unit that calculates a score separation degree on the temporary parameter basis based on a first score representing a similarity degree of a pair of the feature templates extracted from the genuine pair and a second score representing a similarity degree of a pair of the feature templates extracted from the imposter pair and determines the temporary parameter based on a level of the score separation degree. 1. A biometric recognition apparatus comprising:a teacher data generation unit that generates, from a plurality of first biometric images and a plurality of second biometric images, a plurality of pairs each of which is a combination of the first biometric image and the second biometric image;a learning data generation unit that uses a plurality of different temporary parameters to extract feature templates from the first biometric image and the second biometric image; anda calculation unit that calculates a similarity degree of the feature templates extracted from the first biometric image and the second biometric image that become the pair, for the plurality of pairs generated by the teacher data generation unit; andan optimum solution determination unit that determines the temporary parameter based on a calculation result by the calculation unit.2. The biometric recognition apparatus according to claim 1 ,wherein the teacher data generation unit generates a genuine pair and an imposter pair from the plurality ...

Recognition method and device for a target perception data

A data processing method and device includes obtaining target perception data, where the target perception data is any type of the following data, such as image data, video data, or voice data, determining a target scenario to which the target perception data belongs, determining a target perception model corresponding to the target scenario, and computing a recognition result of the target perception data according to the target perception model. A scenario to which perception data belongs is determined, and a recognition result of the perception data is obtained through computation using a perception model corresponding to the scenario. 1. A data processing method , comprising:obtaining target perception data, wherein the target perception data comprises at least one of image data, video data, or voice data;determining a target scenario associated with the target perception data;determining a target perception model corresponding to the target scenario; andcomputing a recognition result of the target perception data according to the target perception model.2. The data processing method of claim 1 , further comprising:performing a scenario analysis on the target perception data; andfurther determining the target scenario based on the scenario analysis.3. The data processing method of claim 2 , wherein the target perception data is associated with a location at which a terminal is currently located claim 2 , and wherein the data processing method further comprises performing the scenario analysis on the target perception data according to positioning information of the location at which the terminal is currently located.4. The data processing method of claim 1 , further comprising:sending, to a server, a first request for a scenario associated with the target perception data; andreceiving the target scenario from the server in response to sending the first request.5. The data processing method of claim 1 , further comprising further determining claim 1 , from a pre- ...

METHOD AND APPARATUS FOR FACILITATING IMPROVED BIOMETRIC RECOGNITION USING IRIS SEGMENTATION

Various methods are provided for facilitating biometric recognition. One example method may comprise receiving an image, the image comprising a plurality of pixels, generating a binary mask image from the image, the binary mask image identifying a plurality of target pixels from among the plurality of pixels, determining a first subset of misclassified target pixels by estimating a first boundary region and identifying a portion of target pixels that are outside of the first boundary region, and determining a second subset of misclassified target pixels by estimating a second boundary region and identifying a portion of target pixels that are within the second boundary region. 1. A method for facilitating biometric recognition , the method comprising:receiving an image, the image comprising a plurality of pixels;generating a binary mask image from the image, the binary mask image identifying a plurality of target pixels from among the plurality of pixels;determining a first subset of misclassified target pixels by estimating a first boundary region and identifying a portion of target pixels that are outside of the first boundary region; anddetermining a second subset of misclassified target pixels by estimating a second boundary region and identifying a portion of target pixels that are within the second boundary region.2. The method according to claim 1 , where in the generation of the binary mask image comprises:applying a label to each of the plurality of pixels of the image, the label identifying each of the plurality of pixels as one of a target pixel or a non-target pixel,wherein the application of the label to each of the plurality of pixels of the image is based on learned parameters; andcausing output of each of the plurality of pixels identified as the plurality of target pixels, the output being the binary mask image.3. The method according to claim 1 , wherein the determination of the first subset of misclassified pixels comprises:receiving the binary ...

System and method for the detection and counting of repetitions of repetitive activity via a trained network

A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from YouTube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.

PREDICTIVE VIRTUAL REALITY DISPLAY SYSTEM WITH POST RENDERING CORRECTION

A virtual reality display system that generates display images in two phases: the first phase renders images based on a predicted pose at the time the display will be updated; the second phase re-predicts the pose using recent sensor data, and corrects the images based on changes since the initial prediction. The second phase may be delayed so that it occurs just in time for a display update cycle, to ensure that sensor data is as accurate as possible for the revised pose prediction. Pose prediction may extrapolate sensor data by integrating differential equations of motion. It may incorporate biomechanical models of the user, which may be learned by prompting the user to perform specific movements. Pose prediction may take into account a user's tendency to look towards regions of interest. Multiple parallel pose predictions may be made to reflect uncertainty in the user's movement. 1. A predictive virtual reality display system with post rendering correction , comprising at least one display viewable by a user;at least one sensor that generates sensor data that measures one or more aspects of a pose of one or more body parts of said user;a pose predictor coupled to said at least one sensor, wherein said pose predictor is configured to calculate a predicted pose of said one or more body parts of said user at a future point in time, based on said sensor data generated by said at least one sensor and based on said future point in time;a 3D model of a scene; obtain an initial predicted pose at a future display update point in time from said pose predictor;', 'calculate one or more 2D projections of said 3D model, based on said initial predicted pose;, 'a scene renderer coupled to said at least one display, said pose predictor, and said 3D model, wherein said scene renderer is configured to'} receive said initial predicted pose at said future display update point in time from said scene renderer;', 'receive said one or more 2D projections from said scene renderer;', ' ...

SYSTEMS AND METHODS FOR PERFORMING FINGERPRINT BASED USER AUTHENTICATION USING IMAGERY CAPTURED USING MOBILE DEVICES

Technologies are presented herein in support of a system and method for performing fingerprint recognition. Embodiments of the present invention concern a system and method for capturing a user's biometric features and generating an identifier characterizing the user's biometric features using a mobile device such as a smartphone. The biometric identifier is generated using imagery captured of a plurality of fingers of a user for the purposes of authenticating/identifying the user according to the captured biometrics and determining the user's liveness. The present disclosure also describes additional techniques for preventing erroneous authentication caused by spoofing. In some examples, the anti-spoofing techniques may include capturing one or more images of a user's fingers and analyzing the captured images for indications of liveness. 1. A method for performing fingerprint recognition , the method comprising:capturing, by a mobile device having a camera, a storage medium, instructions stored on the storage medium, and a processor configured by executing the instructions, images depicting a plurality of fingers of a subject;detecting, with the processor using a finger detection algorithm, the plurality of fingers depicted in one or more of the images;identifying, with the processor from one or more of the images according to a segmentation algorithm, a respective fingertip segment for each finger among the plurality of fingers;extracting, with the processor for each finger, discriminatory features from the respective fingertip segment; andgenerating a biometric identifier including the extracted discriminatory features;storing the generated biometric identifier in the memory with the processor.2. The method of claim 1 , wherein the step of detecting the plurality of fingers comprises sequentially applying a plurality of finger detection algorithms.3. The method of claim 2 , wherein the order in which the plurality of finger detection algorithms is applied is ...

FULLY CONVOLUTIONAL PYRAMID NETWORKS FOR PEDESTRIAN DETECTION

A fully convolutional pyramid network and method for object (e.g., pedestrian) detection are disclosed. In one embodiment, the object detection system is a pedestrian detection system that comprises: a multi-scale image generator to generate a set of images from an input image, the set of images being versions of the input image at different scales; a human body-specific fully convolutional network (FCN) model operable to generate a set of detection results for each image in the set of images that is indicative of objects that are potentially of human bodies; and a post processor to combine sets of detection results generated by the FCN model for the set of images into an output image with each object location determined as potentially being a human body being marked. 1. A visual object detection system comprising:a multi-scale image generator to generate a set of images from an input image, the set of images being versions of the input image at different scales;an object-specific fully convolutional network (FCN) model operable to generate a set of detection results for each image in the set of images that is indicative of objects that are potentially of a specific object type; anda post processor to combine sets of detection results generated by the FCN model for the set of images into an output image with each object location determined as potentially being the specific object type.2. The visual object detection system defined in wherein the object-specific FCN model is generated from a transfer learning guided FCN initially trained using multi-category image dataset followed by application of a fine-tuned process that is based on data of the specific object type.3. The visual object detection system defined in wherein the fine tuning process comprises a multi-phase learning process in which the training set for training the transfer learning guided FCN is refined in each phase after an initial learning phase in the multi-phase learning process claim 2 , wherein ...

Virtual Sensor-Data-Generation System And Method Supporting Development Of Vision-Based Rain-Detection Algorithms

A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual camera through a virtual driving environment comprising at least one virtual precipitation condition and at least one virtual no precipitation condition. During the traversing, the virtual camera may be moved with respect to the virtual driving environment as dictated by a vehicle-motion model modeling motion of a vehicle driving through the virtual driving environment while carrying the virtual camera. Virtual sensor data characterizing the virtual driving environment in both virtual precipitation and virtual no precipitation conditions may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the virtual driving environment in the real world. 1. A method comprising:traversing, by a computer system, one or more virtual cameras over a virtual road surface in a simulation;recording, by the computer system, a plurality of frames of image data corresponding to signals output by the one or more virtual cameras during the traversing; andconverting, by the computer system, the plurality of frames to training data by annotating each frame thereof with ground-truth data indicating whether virtual precipitation is present therewithin.2. The method of claim 1 , wherein the annotating comprises annotating each frame of the plurality of frames with ground-truth data indicating whether virtual rain is present therewithin.3. The method of claim 2 , wherein the traversing comprises moving each of the one or more virtual cameras with respect to the virtual road surface as dictated by a vehicle-motion model modeling motion of a vehicle carrying the one or more virtual cameras and driving on the virtual road surface.4. The method of claim 3 , wherein the traversing comprises traversing in the simulation the one or more virtual cameras over the virtual road surface and ...

VISUAL RECOGNITION USING DEEP LEARNING ATTRIBUTES

A processing device for performing visual recognition using deep learning attributes and method for performing the same are described. In one embodiment, a processing device comprises: an interface to receive an input image; and a recognition unit coupled to the interface and operable to perform visual object recognition on the input image, where the recognition unit has an extractor to extract region proposals from the input image, a convolutional neural network (CNN) to compute features for each extracted region proposal, the CNN being operable to create a soft-max layer output, a cross region pooling unit operable to perform pooling of the soft-max layer output to create a set of attributes of the input image, and an image classifier operable to perform image classification based on the attributes of the input image. 1. A processing device , the processing device comprising:an interface to receive an input image; and an extractor to extract region proposals from the input image;', 'a convolutional neural network (CNN) to compute features for each extracted region proposal, the CNN being operable to create a soft-max layer output;', 'a cross region pooling unit operable to perform pooling of the soft-max layer output to create a set of attributes of the input image; and', 'an image classifier operable to perform image classification based on the attributes of the input image., 'a recognition unit coupled to the interface and operable to perform visual object recognition on the input image, the recognition unit having'}2. The processing device defined in wherein the soft-max layer output comprises regional neural codes.3. The processing device defined in wherein the cross region pooling unit performs pooling of the output of the soft-max layer of the CNN by performing a cross-region max-pooling of regional neural codes from the output of the soft-max layer of the CNN.4. The processing device defined in wherein the cross region pooling unit performs cross-region max ...

IDENTIFYING VISUAL STORM SIGNATURES FROM SATELLITE IMAGES

Satellite images from vast historical archives are analyzed to predict severe storms. We extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. The method extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts. A preferred method applies cloud motion estimation in image sequences. This aspect of the invention is important because it extracts and models certain patterns of cloud motion, in addition to capturing the cloud displacement. 115.-. (canceled)16. A system for automatic storm detection or prediction based on historical meteorological data and satellite image sequences , for validating and complementing conventional forecasts , the historical meteorological data includes historical storm reports and satellite image archives for historical weather , the system comprising:a processing device; and provide a digital computer;', 'receive a sequence of satellite images involving a geographical region;', 'receive historical meteorological data associated with the geographical region;', 'automatically extract visual features indicative of storm signatures from the sequence of satellite images;', 'learn the correspondences between the visual storm signatures and the occurrences of current and future storms based on the historical meteorological data; and', 'detect or predict storms in the geographical region using the learned correspondences., 'a non-transitory, processor-readable storage medium, the non-transitory, processor readable storage medium comprising one or more programming instructions thereon that, when executed, cause the ...

Lens distortion correction using a neurosynaptic circuit

One or more embodiments provide a neurosynaptic circuit that includes multiple neurosynaptic core circuits that: perform image sharpening by converting a source image to a sharpened destination image by: taking as input a sequence of image frames of a video with one or more channels per frame, and representing the intensity of each pixel of each channel of each frame as neural spikes; processing the source image to obtain the sharpened destination image for a particular frame and channel that enhances certain high frequency components of the source image; and processing neural spike representations of the destination image for outputting a spike representation of the sharpened destination image.

SIMULATING IMAGE CAPTURE

The present disclosure relates to simulating the capture of images. In some embodiments, a document and a camera are simulated using a three-dimensional modeling engine. In certain embodiments, a plurality of images are captured of the simulated document from a perspective of the simulated camera, each of the plurality of images being captured under a different set of simulated circumstances within the three-dimensional modeling engine. In some embodiments, a model is trained based at least on the plurality of images which determines at least a first technique for adjusting a set of parameters in a separate image to prepare the separate image for optical character recognition (OCR). 1. A computer-implemented method for simulating the capture of images , comprising:simulating a document and a camera using a three-dimensional modeling engine;capturing a plurality of images of the simulated document from a perspective of the simulated camera, each of the plurality of images being captured under a different set of simulated circumstances within the three-dimensional modeling engine;training a model based at least on the plurality of images, wherein the trained model determines at least a first technique for adjusting a set of parameters in a separate image to prepare the separate image for optical character recognition (OCR).2. The computer-implemented method of claim 1 , wherein the simulated circumstances include at least one of: lighting; background; and camera pose.3. The computer-implemented method of claim 2 , wherein the camera pose includes yaw claim 2 , pitch claim 2 , roll claim 2 , and height.4. The computer-implemented method of claim 1 , further comprising:determining, based on the trained model, whether a quality of the separate image can be improved to an acceptable level for the OCR, wherein the quality of the separate image is based on one or more of the set of parameters.5. The computer-implemented method of claim 4 , wherein determining whether the ...

OBJECT DETECTION USING NEURAL NETWORK SYSTEMS

Systems, methods, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a system includes initial neural network layers configured to: receive an input image, and process the input image to generate a plurality of first feature maps that characterize the input image; a location generating convolutional neural network layer configured to perform a convolution on the representation of the first plurality of feature maps to generate data defining a respective location of each of a predetermined number of bounding boxes in the input image, wherein each bounding box identifies a respective first region of the input image; and a confidence score generating convolutional neural network layer configured to perform a convolution on the representation of the first plurality of feature maps to generate a confidence score for each of the predetermined number of bounding boxes in the input image. 1. A system comprising: receive an input image, and', the plurality of first feature maps are each of the same size,', 'each of the plurality of first feature maps have a respective value at each of a plurality of first feature map locations, and', 'each of the plurality of first feature map locations correspond to a respective first region in the input image;, 'process the input image to generate a plurality of first feature maps that characterize the input image, wherein], 'one or more initial neural network layers, wherein the one or more initial neural network layers are configured to receive a representation of the first plurality of feature maps, and', 'perform a convolution on the representation of the first plurality of feature maps to generate data defining a respective location of each of a predetermined number of bounding boxes in the input image, wherein each bounding box identifies a respective first region of the input image; and, 'a location generating convolutional neural network layer, wherein the location generating ...

Method and System for Clinical Decision Support with Local and Remote Analytics

A method and system for non-invasive medical image based assessment of coronary artery disease (CAD) for clinical decision support using on-site and off-site processing is disclosed. Medical image data of a patient is received. A processing strategy for assessing CAD of the patient using one of on-site processing, off-site processing, or joint on-site and off-site processing is automatically selected based on clinical requirements for a current clinical scenario. Non-invasive assessment of CAD of the patient is performed based on the medical image data of the patient using one of on-site processing, off-site-processing, or joint on-site and off-site processing according to the selected processing strategy. A final assessment of CAD of the patient is output based on the non-invasive assessment of CAD. 1. A method for non-invasive assessment of coronary artery disease (CAD) of a patient , comprising:receiving medical image data of a patient;automatically selecting a processing strategy for assessing CAD of the patient using one of on-site processing, off-site processing, or joint on-site and off-site processing based on clinical requirements for a current clinical scenario;performing non-invasive assessment of CAD of the patient based on the medical image data of the patient using one of on-site processing, off-site-processing, or joint on-site and off-site processing according to the selected processing strategy; andoutputting a final assessment of CAD of the patient based on the non-invasive assessment of CAD.2. The method of claim 1 , wherein automatically selecting a processing strategy for assessing CAD of the patient using one of on-site processing claim 1 , off-site processing claim 1 , or joint on-site and off-site processing based on clinical requirements for a current clinical scenario comprises:automatically selecting the processing strategy for assessing CAD of the patient using one of on-site processing, off-site processing, or joint on-site and off-site ...

SYSTEMS AND METHODS FOR PREDICTING LOCATION, ONSET, AND/OR CHANGE OF CORONARY LESIONS

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model. 129-. (canceled)30. A computer-implemented method for predicting information relating to a vascular lesion of a patient , the method comprising:creating a feature vector comprising characteristics at each of a plurality of points in a geometric model, for each of a plurality of individuals;associating each feature vector with an estimate of a probability of plaque growth, shrinkage, or onset, for each of the plurality of points in the geometric model, for each of the plurality of individuals;creating a feature vector comprising characteristics at each of a plurality of points in a patient-specific geometric model of a patient's vasculature, for a patient different from the plurality of individuals;comparing the patient-specific geometric model of the patient's vasculature to the geometric models of the plurality of individuals, to identify feature vectors common between the patient and each of the plurality of individuals;identifying a selected point of a geometric model of a ...

Camera Pose Estimation Method for Augmented Reality Manual For Cars

Embodiments of the present disclosure are directed to an augmented reality based user's manual for a vehicle implemented as an application on a mobile device which allows the user to point a mobile phone, tablet or an augmented reality headset at any part of the vehicle interior or exterior and experience augmented annotations, overlays, popups, etc. displayed on images of real parts of the car captured by the user's mobile device. Embodiments provide for estimating the camera pose in six degrees of freedom based on the content of the captured image or video and using a neural network trained on a dense sampling of a three-dimensional model of the car rendered with realistic textures to identify and properly align the augmented reality presentation with the image of the vehicle being captured by the mobile device. 1. A method for providing an augmented reality user manual for a vehicle , the method comprising:creating, by one or more processors, a three-dimensional model of the vehicle and content of the user manual;training, by the one or more processors, a neural network to recognize a plurality of views of the vehicle and associate each view with a portion of the three-dimensional model of the vehicle; andrendering, by a mobile device, a portion of the three-dimensional model of the vehicle and a portion of the content of the user manual overlaid on an image of the vehicle captured by a camera of the mobile device based on the image of the vehicle captured by the camera of the mobile device and the association of one or more views with one or more portions of the three-dimensional model of the vehicle in the trained neural network.2. The method of claim 1 , wherein creating the three-dimensional model of the vehicle and the content of the user manual further comprises:creating a three-dimensional model of the interior of the vehicle and a three-dimensional model of the exterior of the vehicle;identifying one or more features of the vehicle;creating a three- ...

TRANSPORTATION PREDICTION SYSTEM AND METHOD

In one embodiment, an automotive prediction system includes a processing circuitry to obtain labels labelling media content elements identified in road-scene media content sequences, each label including a content descriptor selected from different content descriptors describing at least one media content element, the different content descriptors including a first and second content descriptor, calculate a correlation of the first and second content descriptor based on a count of occurrences of the first content descriptor being used for labelling after, but within a given temporal distance of the first content descriptor being used for labelling in the road-scene media content sequences, and populate an automotive prediction database with the correlation of the first and second content descriptor for use in making decisions during driving of a vehicle. Related apparatus and methods are also described. 1. An automotive prediction system comprising: a processing circuitry; and a memory to store data used by the processing circuitry , the processing circuitry being configured to:obtain a plurality of labels labelling a plurality of media content elements identified in a plurality of road-scene media content sequences being at least one of audio or video road-scene sequences, each one label of the plurality of labels including a content descriptor selected from a plurality of different content descriptors describing at least one media content element of the plurality of media content elements, the plurality of different content descriptors including a first content descriptor and a second content descriptor;calculate a correlation of the first content descriptor and the second content descriptor based on a first count of occurrences of the second content descriptor being used for labelling in the plurality of road-scene media content sequences after, but within a given temporal distance of, the first content descriptor being used for labelling in the plurality of road ...

TRAINING IMAGE SIGNAL PROCESSORS USING INTERMEDIATE LOSS FUNCTIONS

In an example method for training image signal processors, a reconstructed image is generated via an image signal processor based on a sensor image. An intermediate loss function is generated based on a comparison of an output of one or more corresponding layers of a computer vision network and a copy of the computer vision network. The output of the computer vision network is based on the reconstructed image. An image signal processor is trained based on the intermediate loss function. 1. An apparatus comprising:an image signal processor to generate a reconstructed image based on a sensor image;a generator to implement a first loss function based on comparison of an intermediate layer of a first computer vision network with a corresponding intermediate layer of a second computer vision network, the first computer vision network to process the reconstructed image, the second computer vision network to process an input image associated with the sensor image; anda parameter modifier to modify a parameter of the image signal processor based on at least one of the first loss function or a second loss function, the second loss function based on the first loss function.2. The apparatus of claim 1 , wherein the second computer vision network is initialized to be a copy of the first computer vision network.3. The apparatus of claim 1 , further including a modeler to apply at least one of a color filter array claim 1 , a point spread function or noise to the input image to generate the sensor image.4. The apparatus of claim 1 , wherein the comparison is based on a mean square difference between a first set of results from the intermediate layer of the first computer vision network and a corresponding second set of results from the corresponding intermediate layer of the second computer vision network.5. The apparatus of claim 1 , wherein the generator is a first generator claim 1 , and further including a second generator to generate the second loss function based on the ...

Generating Search Determinations for Assortment Planning Using Visual Sketches

Methods, systems, and computer program products for generating search determinations for assortment planning and buying using visual sketches are provided herein. A computer-implemented method includes processing a query image by identifying one or more visual features in the query image and applying at least one nearest neighbor algorithm to the one or more identified visual features; identifying, from one or more databases, multiple images based at least in part on the processing; generating a result set by applying one or more smoothing algorithms to the multiple identified images; generating at least one sketch based at least in part on the result set; and outputting the at least one generated sketch to one or more users via a user interface. 1. A computer-implemented method , the method comprising steps of:processing a query image by (i) identifying one or more visual features in the query image and (ii) applying at least one nearest neighbor algorithm to the one or more identified visual features;identifying, from one or more databases, multiple images based at least in part on said processing;generating a result set by applying one or more smoothing algorithms to the multiple identified images;generating at least one sketch, based at least in part on the result set; andoutputting the at least one generated sketch to one or more users via a user interface;wherein the steps are carried out by at least one computing device.2. The computer-implemented method of claim 1 , wherein said identifying one or more visual features in the query image comprises applying one or more models to the query image.3. The computer-implemented method of claim 2 , wherein the one or more models comprise one or more convolutional neural networks.4. The computer-implemented method of claim 2 , wherein the one or more models comprise one or more residual neural networks.5. The computer-implemented method of claim 1 , wherein the one or more databases comprise one or more product ...

EVALUATION ASSISTANCE METHOD, EVALUATION ASSISTANCE SYSTEM, AND COMPUTER-READABLE MEDIUM

An evaluation assistance method includes: acquiring a first image to be used for performance evaluation of trained models; generating a plurality of second images, each of the plurality of second images being a result of processing the first image by each of a plurality of trained models; and displaying each of the plurality of trained models in association with a corresponding second image of the plurality of second images. 1. An evaluation assistance method comprising:acquiring a first image to be used for performance evaluation of trained models;generating a plurality of second images, each of the plurality of second images being a result of processing the first image by each of a plurality of trained models; anddisplaying each of the plurality of trained models in association with a corresponding second image of the plurality of second images.2. The evaluation assistance method of claim 1 , whereinthe acquiring the first image includes acquiring a plurality of first images that include the first image,the generating the plurality of second images includes generating a plurality of second images for each of the plurality of first images, andthe displaying each of the plurality of trained models includes displaying each of the plurality of trained models in association with at least one corresponding second image of the plurality of second images.3. The evaluation assistance method of claim 2 , whereinthe displaying each of the plurality of trained models includes displaying each of the plurality of trained models in association with at least one second image and at least one first image corresponding to the at least one second image, andthe at least one first image is included in the plurality of first images.4. The evaluation assistance method of claim 2 , whereinthe displaying each of the plurality of trained models includes displaying each of the plurality of trained models in association with at least one second image and metadata associated with a ...

IMAGE PROCESSING DEVICE, INFORMATION GENERATION DEVICE, IMAGE PROCESSING METHOD, INFORMATION GENERATION METHOD, AND COMPUTER READABLE MEDIUM

A feature value extraction section extracts a feature value from a pixel or a group of pixels of a sampling point for every plurality of sampling points for a reference point with respect to a region point on an image, and extracts a group of feature values with respect to the reference point; the location information identification section references an LRF function indicating a correspondence of the group of feature values with respect to the reference point and the location information indicating a relative location of the region point with respect to the reference point to identify the location information corresponding to the group of feature values extracted by the feature value extraction section, and the region point identification section assumes the location indicated by the location information identified by the location information identification section as a region point of the object. 1. An image processing device configured to detect a region point of an object from an image , the image processing device comprising:a reference point identification section configured to identify a reference point with respect to the region point on the image;a feature value extraction section configured to extract a feature value from a pixel of a sampling point or a group of pixels comprising the pixel for every plurality of sampling points with respect to the reference point, and to extract a group of feature values with respect to the reference point configured by the extracted plurality of feature values corresponding to each of the sampling points;a location information identification section configured:to reference correspondence information indicating a correspondence of the group of feature values with respect to the reference point extracted from each pixel or each group of pixels of the plurality of sampling points and location information indicating a relative location of the region point with respect to the reference point, and to identify location ...

SYSTEM FOR OPTIMIZING PLATFORM SETTINGS BASED ON CROP STATE CLASSIFICATION

A system for controlling an operative parameter of a harvesting platform of an agricultural harvesting machine comprises a sensor adapted to provide downed crop information indicative of a characteristic of downed crop in a field to be harvested and an electronic control unit adapted to control an operative parameter of at least one of a reel and a cutter bar table position of the harvesting platform based upon the downed crop information. 1. A system for controlling an operative parameter of a harvesting platform of an agricultural harvesting machine , comprising:a sensor adapted to provide downed crop information indicative of a characteristic of downed crop in a field to be harvested; andan electronic control unit adapted to control an operative parameter of at least one of a reel and a cutter bar table position of the harvesting platform based upon the downed crop information.2. The system of claim 1 , wherein the characteristic of the downed crop is at least one of an orientation of the downed crop and a magnitude of the downed crop.3. The system of claim 1 , wherein the operative parameter of the reel is at least one of vertical reel position claim 1 , horizontal reel position and reel speed.4. The system of claim 1 , wherein the electronic control unit is adapted to control at least one of cut height of the harvesting platform claim 1 , platform fore-aft tilt and harvesting machine propelling speed based upon the downed crop information.5. The system of claim 1 , wherein the electronic control unit is adapted to control the cutter bar table position of the harvesting platform based on a sensed crop height above ground and an actual cutting height of the harvesting platform such that a distance between a knife bar and a transverse conveyor of the harvesting platform at least approximately corresponds to a difference between the sensed crop height above ground and an actual cutting height of the harvesting platform.6. The system of claim 1 , wherein the ...

REAR CAMERA STUB DETECTION

A method for detecting stubs or intersecting roadways includes receiving perception data from at least two sensors. The at least two sensors include a rear facing camera of a vehicle and another sensor. The perception data includes information for a current roadway on which the vehicle is located. The method includes detecting, based on the perception data, an intersecting roadway connecting with the current roadway. The method also includes storing an indication of a location and a direction of the intersecting roadway with respect to the current roadway. 1. A method comprising:receiving perception data from at least two sensors, the at least two sensors comprising a rear facing camera of a vehicle, wherein the perception data comprises information for a current roadway on which the vehicle is located;detecting, based on the perception data, an intersecting roadway connecting with the current roadway; andstoring an indication of a location and a direction of the intersecting roadway with respect to the current roadway.2. The method of claim 1 , wherein detecting the intersecting roadway comprises detecting one or more of: a gap in roadway markings claim 1 , a break in a shoulder for the current roadway claim 1 , or a variation or break in curb or barrier height.3. The method of claim 1 , wherein detecting the intersecting roadway comprises detecting using a deep neural network.4. The method of claim 1 , wherein the at least two sensors comprise the rear facing camera and one or more of a light detection and ranging (LIDAR) system claim 1 , a radar system claim 1 , an ultrasound sensing system claim 1 , or an infrared camera system.5. The method of claim 1 , wherein the direction indicates a side of the current roadway on which the intersecting roadway is located.6. The method of claim 1 , wherein storing the indication of the location and direction comprises uploading to a remote storage location accessible over a network.7. The method of claim 7 , further ...

SYSTEMS AND METHODS FOR IDENTIFYING USERS IN MEDIA CONTENT BASED ON POSELETS AND NEURAL NETWORKS

Systems, methods, and non-transitory computer-readable media can receive a first image including a representation of a first user. A second image including a representation of a second user can be received. A first set of poselets associated with the first user can be detected in the first image. A second set of poselets associated with the second user can be detected in the second image. The first image including the first set of poselets can be inputted into a first instance of a neural network to generate a first multi-dimensional vector. The second image including the second set of poselets can be inputted into a second instance of the neural network to generate a second multi-dimensional vector. A first distance metric between the first multi-dimensional vector and the second multi-dimensional vector can be determined. 1. A computer-implemented method comprising:determining, by a computing system, a first distance between a first value and a second value, wherein the first value is based on provision of a first image including a first set of poselets associated with a first user into a first instance of a neural network and the second value is based on provision of a second image including a second set of poselets associated with the first user into a second instance of the neural network;determining, by the computing system, a second distance between the first value and a third value, wherein the third value is based on provision of a third image including a third set of poselets associated with a second user into a third instance of the neural network; andtraining, by the computing system, the neural network to cause the first distance to be less than the second distance.2. The computer-implemented method of claim 1 , wherein at least one of the first value claim 1 , the second value claim 1 , and the third value is a multi-dimensional vector.3. The computer-implemented method of claim 1 , wherein the first image corresponds to a query image claim 1 , wherein ...

METHODS AND APPARATUS FOR CAPTURING, PROCESSING, TRAINING, AND DETECTING PATTERNS USING PATTERN RECOGNITION CLASSIFIERS

A system, methods, and apparatus for generating pattern recognition classifiers are disclosed. An example method includes identifying graphical objects within an image of a card object, for each identified graphical object: i) creating a bounding region encompassing the graphical object such that a border of the bounding region is located at a predetermined distance from segments of the graphical object, ii) determining pixels within the bounding region that correspond to the graphical object, iii) determining an origin of the graphical object based on an origin rule, iv) determining a text coordinate relative to the origin for each determined pixel, and v) determining a statistical probability that features arc present within the graphical object, each of the features including at least one pixel having text coordinates and for each graphical object type, combining the statistical probabilities for each of the features of the identified graphical objects into a classifier data structure. 1. A method to generate graphical object classifier data structures comprising:identifying graphical objects within an image recorded by a camera; i) creating a bounding region encompassing the graphical object such that a border of the bounding region is located at a predetermined distance from segments of the graphical object;', 'ii) determining pixels within the bounding region that correspond to the graphical object;', 'iii) determining an origin of the graphical object based on at least one origin rule;', 'iv) determining a text coordinate relative to the origin for each determined pixel; and', 'v) determining a statistical probability that features are present within the graphical object, each of the features including at least one pixel having text coordinates; and, 'for each identified graphical object within the imagefor each graphical object type, combining the statistical probabilities for each of the features of the identified graphical objects into a classifier data ...

IDENTIFYING PRESENTATION STYLES OF EDUCATIONAL VIDEOS

The presentation style of a video is identified. A set of features that represents the video is computed. A pre-learned video presentation style classifier is then used to weight each of the features in the set of features and determine a presentation style that is predominately employed in the video based on the weighting of the features. 1. A computer-implemented process for identifying the presentation style of a video , comprising the actions of:using one or more computing devices that are in communication with each other via a computer network to perform the following process actions:receiving the video;computing a set of features that represents the video; andusing a pre-learned video presentation style classifier to weight each of the features in said set and determine a presentation style that is predominately employed in the video, said presentation style determination being based on the weighting of said features.2. The process of claim 1 , wherein the determined presentation style comprises a one of the presentation styles in a set of possible presentation styles comprising:a rendered video class of presentation styles; anda real-world video class of presentation styles.3. The process of claim 2 , wherein the video comprises an educational video claim 2 , and the rendered video class of presentation styles comprises one or more of:a rendered slide show presentation style; ora rendered slide show comprising a video of a presenter presentation style; ora rendered animation presentation style; ora rendered photographs presentation style; ora rendered hand-drawn slides presentation style.4. The process of claim 2 , wherein the video comprises an educational video claim 2 , and the real-world video class of presentation styles comprises one or more of:a natural video presentation style; ora video of an interview presentation style; ora video of handwriting on paper presentation style; ora video of projected slides presentation style; ora video of a whiteboard ...

AUTOMATED LEARNING AND GESTURE BASED INTEREST PROCESSING

A system, method and program product for processing user interests. A system is provided that includes: a gesture management system that receives gesture data from a collection device for an inputted interest of a user; a pattern detection system that receives and analyzes behavior data associated with the inputted interest; an interest affinity scoring system that calculates an affinity score for the inputted interest based on the gesture data and an analysis of the behavior data; a dynamic classification system that assigns a dynamically generated tag to the inputted interest based on an inputted context associated with the inputted interest; and a user interest database that stores structured interest information for the user, including a unique record for the inputted interest that includes the affinity score and dynamically generated tag. 1. A system for processing user interests , comprising:a gesture management system that receives gesture data from a collection device for an inputted interest of a user;a pattern detection system that receives and analyzes behavior data associated with the inputted interest;an interest affinity scoring system that calculates an affinity score for the inputted interest based on the gesture data and an analysis of the behavior data;a dynamic classification system that assigns a dynamically generated tag to the inputted interest based on an inputted context associated with the inputted interest; anda user interest database that stores structured interest information for the user, including a unique record for the inputted interest that includes the affinity score and dynamically generated tag.2. The system of claim 1 , wherein the gesture management system determines if the gesture data includes a predefined gesture correlated to a first indicator of relevance to the user.3. The system of claim 2 , wherein the pattern detection system determines if the behavior data matches one of a set of patterns of known behaviors stored in ...

USER ADAPTIVE IMAGE COMPENSATOR

A user adaptive image compensator includes a feature extractor, a compensated image generator, an image selector, and a preference parameter updater. The feature extractor extracts features from an input image. The compensated image generator generates compensated preference parameters based on a preference parameter. The compensated image generator generates a plurality of compensated images by compensating the input image based on the compensated preference parameters. The image selector displays the compensated images to a user. The image selector outputs a selected compensated image, which is selected from the compensated images by the user, as an output image. The image selector outputs a selected compensated preference parameter from the compensated preference parameters and which corresponds to the selected compensated image. The preference parameter updater updates the preference parameter based on the selected compensated preference parameter and the extracted features. 1. A user adaptive image compensator , comprising:a feature extractor configured to extract features from an input image;a compensated image generator configured to generate compensated preference parameters based on a preference parameter, the compensated image generator configured to generate a plurality of compensated images of the input image based on the generated compensated preference parameters;an image selector configured to display the compensated images to a user, the image selector configured to output a selected compensated image, which is selected by the user from the displayed compensated images, as an output image, the image selector being further configured to output a selected compensated preference parameter that corresponds to the selected compensated image; anda preference parameter updater configured to update the preference parameter based on the selected compensated preference parameter and the extracted features.2. The user adaptive image compensator of claim 1 , ...

TEMPORALLY SMOOTH VIDEO ENHANCEMENT

Implementations generally relate to enhancing a video. In some implementations, a method includes classifying one or more objects in one or more frames of the video. The method further includes determining one or more filter parameters of one or more filters based on the classifying of the one or more objects. The method further includes smoothing one or more of the determined filter parameters based on the classifying of the one or more objects. The method further includes applying one or more of the filters with corresponding smoothed filter parameters to one or more frames of the video. 1. A computer-implemented method to process video , the method comprising:classifying one or more objects in a plurality of frames of the video;determining one or more filter parameters of one or more filters based on the classifying of the one or more objects;smoothing the one or more determined filter parameters based on the classifying of the one or more objects, wherein smoothing the one or more determined filter parameters includes adjusting one or more magnitudes of the one or more determined filter parameters across adjacent frames of the plurality of frames; andapplying the one or more filters to the plurality of frames of the video to modify one or more pixels of the plurality of frames of the video, wherein applying the one or more filters includes inputting the one or more smoothed filter parameters to the one or more filters and determining pixel values from the one or more filters.2. The method of claim 1 , further comprising tracking one or more of the objects across the plurality of frames of the video claim 1 , wherein the smoothing of the one or more determined filter parameters is based on the tracking of the one or more tracked objects.3. The method of claim 1 , wherein adjusting one or more magnitudes of the one or more determined filter parameters across adjacent frames of the plurality of frames includes adjusting one or more thresholds used to determine the ...

MULTI-SOURCE MULTI-MODAL ACTIVITY RECOGNITION IN AERIAL VIDEO SURVEILLANCE

Multi-source multi-modal activity recognition for conducting aerial video surveillance comprising detecting and tracking multiple dynamic targets from a moving platform, representing FMV target tracks and chat-messages as graphs of attributes, associating FMV tracks and chat-messages using a probabilistic graph based mapping approach; and detecting spatial-temporal activity boundaries. 1. A system for multi-source multi-modal activity recognition in conducting aerial video surveillance comprising:from a moving platform, detecting and tracking, with a video imager, multiple dynamic targets;recording analyst call outs or chats, and appending said analyst call outs or chats to a file;representing full motion video (FMV) target tracks and chat-message as graphs of attributes;associating said FMV tracks and said chat-messages using a probabilistic graph based mapping approach;detecting spatial-temporal activity boundaries;categorizing activity of said detected multiple dynamic targets; andon a display, presenting said activity.2. The system of claim 1 , wherein said detecting and tracking multiple dynamic targets from a moving platform comprises:differencing registered frames;using high pixel intensity difference point features to establish correspondences between other points in a previous frame; andclustering point-velocity pairs into motion regions assumed to be individual targets.3. The system of claim 1 , wherein said step of representing FMV target tracks and chat-message as graphs of attributes comprises:dividing tracks into segments;representing attributes of targets as nodes;characterizing relationships between said nodes as edges; andchat parsing.4. The system of claim 1 , wherein said step of associating FMV tracks and chat-messages comprise probabilistic matching comprising:extracting a chat-message and all video tracks in a given time interval from data sets;generating graph representations of video-tracks and chat messages; andperforming partial graph ...

LOGIN ACCESS CONTROL FOR SECURE/PRIVATE DATA

A login access control system is provided. The login access control system includes a camera configured to capture an input image of a subject purported to be a person and attempting to login to a system to access secure data. The login access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The login access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the secure data and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task. 1. A login access control system , comprising:a camera configured to capture an input image of a subject purported to be a person and attempting to login to a system to access secure data;a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task; anda processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the secure data and a liveness of the subject, and wherein the liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.2. The login access control system of claim 1 , wherein the processor is further configured to control access to the secure data claim 1 , responsive to results of the liveness detection task and the face recognition task.3. The login access control system of claim 1 , ...

Object Detection System and Object Detection Method

A method detects an object in an image. The method extracts a first feature vector from a first region of an image using a first subnetwork and determines a second region of the image by processing the first feature vector with a second subnetwork. The method also extracts a second feature vector from the second region of the image using the first subnetwork and detects the object using a third subnetwork on a basis of the first feature vector and the second feature vector to produce a bounding region surrounding the object and a class of the object. The first subnetwork, the second subnetwork, and the third subnetwork form a neural network. Also, a size of the first region differs from a size of the second region. 1. A method for detecting an object in an image , comprising:extracting a first feature vector from a first region of an image using a first subnetwork;determining a second region of the image by processing the first feature vector with a second subnetwork, wherein a size of the first region differs from a size of the second region;extracting a second feature vector from the second region of the image using the first subnetwork; anddetecting the object using a third subnetwork based on the first feature vector and the second feature vector to produce a bounding box surrounding the object and a class of the object, wherein the first subnetwork, the second subnetwork, and the third subnetwork form a neural network, wherein steps of the method are performed by a processor.2. The method of claim 1 , wherein the second subnetwork is a deep recurrent neural network.3. The method of claim 2 , wherein the deep recurrent neural network is a stacked recurrent neural network.4. The method of claim 3 , wherein the stacked recurrent neural network is formed by two hidden layers.5. The method of claim 1 , wherein the third subnetwork performs an element-wise max operation using the first feature vector and the second feature vector.6. The method of claim 1 , further ...

GENERATING IMAGES USING NEURAL NETWORKS

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating images using neural networks. One of the methods includes generating the output image pixel by pixel from a sequence of pixels taken from the output image, comprising, for each pixel in the output image, generating a respective score distribution over a discrete set of possible color values for each of the plurality of color channels. 1. A neural network system implemented by one or more computers , the neural network system being configured to receive a neural network input and to generate an output image from the neural network input , the output image comprising a plurality of pixels arranged in a two-dimensional map , each pixel having a respective color value for each of a plurality of color channels , and the neural network system comprising:one or more initial neural network layers configured to receive the neural network input and to process the neural network input to generate an alternative representation of the neural network input; andone or more output layers, wherein the output layers are configured to receive the alternative representation and to generate the output image pixel by pixel from a sequence of pixels taken from the output image, comprising, for each pixel in the output image, generating a respective score distribution over a discrete set of possible color values for each of the plurality of color channels.2. The neural network system of claim 1 , wherein the plurality of color channels are ordered claim 1 , wherein the one or more output layers comprise a respective output layer corresponding to each of the plurality of color channels claim 1 , and wherein each of the output layers is configured to claim 1 , for each pixel of the output image:generate the respective score distribution over the discrete set of possible color values for the color channel corresponding to the output layer conditioned on (i) color values for pixels ...

Audio-Visual Speech Recognition with Scattering Operators

Aspects described herein are directed towards methods, computing devices, systems, and computer-readable media that apply scattering operations to extracted visual features of audiovisual input to generate predictions regarding the speech status of a subject. Visual scattering coefficients generated according to one or more aspects described herein may be used as input to a neural network operative to generate the predictions regarding the speech status of the subject. Predictions generated based on the visual features may be combined with predictions based on audio input associated with the visual features. In some embodiments, the extracted visual features may be combined with the audio input to generate a combined feature vector for use in generating predictions. 1. A method comprising:receiving, by a computing device, audiovisual input comprising audio input and video input associated with a subject;extracting, by the computing device, visual features from the video input;applying, by the computing device, a scattering operation to the extracted visual features to generate a vector of scattering coefficients;providing the vector of scattering coefficients as input to a first neural network for visual processing;providing the audio input to a second neural network for audio processing;combining, by the computing device, a first output of the first neural network with a second output of the second neural network to generate a fused audiovisual feature vector based on the audiovisual input;providing the fused audiovisual feature vector to a third neural network for audiovisual processing; andgenerating, by the computing device and using the third neural network, a first prediction regarding a speech status of the subject based on the fused audiovisual feature vector.2. The method of claim 1 , wherein providing the vector of scattering coefficients as input to the first neural network for visual processing comprises:normalizing the vector of scattering coefficients ...

AUTOMATIC GENERATION OF SEMANTIC-BASED CINEMAGRAPHS

Described are various technologies that pertain to automatically generating looped videos or cinemagraphs by selecting objects to animate from an input video. In one implementation a group of semantically labeled objects from an input video is received. Candidate objects from the input video that can appear as a moving object in an output cinemagraph or looped video are identified. Candidate video loops are generated using the selected candidate objects. One or more of these candidate video loops are then selected to create a final cinemagraph. The selection of candidate video loops used to create the final cinemagraph can be made by a user or by a predictive model trained to evaluate the subjective attractiveness of the candidate video loops. 1. A system for generating a cinemagraph of one or more video loops , comprising: receiving an input video, wherein the input video comprises a sequence of frames each frame of which comprises pixels,', 'if the input video is not semantically segmented, semantically segmenting the frames of the input video to identify regions in the frames that correspond to semantic objects,', 'selecting semantic objects as candidate objects to animate;', 'generating candidate loops of the selected candidate objects;', 'selecting one or more of the candidate loops to be used to create the cinemagraph; and', 'creating a cinemagraph from the selected candidate loops using the input time intervals computed for the pixels of the frames of the input video, wherein the cinemagraph exhibits regions that appear static to a viewer and regions comprising dynamic video loops of the selected candidate objects that appear to the viewer to be changing over time., 'a cinemagraph generator comprising one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices, and a computer program having a plurality of sub-programs executable by said computing devices ...

AUTOMATIC CONTROL OF WEARABLE DISPLAY DEVICE BASED ON EXTERNAL CONDITIONS

Embodiments of a wearable device can include a head-mounted display (HMD) which can be configured to display virtual content. While the user is interacting with visual or audible virtual content, the user of the wearable may encounter a triggering event such as, for example, an emergency condition or an unsafe condition, detecting one or more triggering objects in an environment, or determining characteristics of the user's environment (e.g., home or office). Embodiments of the wearable device can automatically detect the triggering event and automatically control the HMD to deemphasize, block, or stop displaying the virtual content. The HMD may include a button that can be actuated by the user to manually deemphasize, block, or stop displaying the virtual content. 1. A wearable system configured to display virtual content in a mixed reality or virtual reality environment , the wearable system comprising:a display configured to present virtual content in a mixed reality, augmented reality, or virtual reality environment; and receive images of an environment of a user;', 'cause to be rendered by the display a plurality of virtual content items associated with the environment of the user;', 'analyze the image using one or more object recognizers configured to recognize objects in the environment with machine learning algorithms;', 'detect a triggering event based at least partly on an analysis of the image; and', access content blocking rules associated with the environment, wherein the content blocking rules comprise a blacklist indicating virtual content items that are available for muting;', 'determine, based on the content blocking rules associated with the environment, one or more of the plurality of virtual content items that are available for muting in the environment; and', 'mute the determined one or more virtual content., 'in response to a detection of the triggering event], 'a hardware processor programmed to2. The wearable system of claim 1 , wherein the ...