УСТРОЙСТВО И СПОСОБ ФОРМИРОВАНИЯ КАРТЫ КОРРЕКЦИИ ЗАТУХАНИЯ

Использование: для формирования карты коррекции затухания. Сущность изобретения заключается в том, что блок обеспечения изображений обеспечивает изображение объекта, содержащего различные категории элементов, и блок сегментации применяет сегментацию к изображению для формирования сегментированного изображения, содержащего области изображения, соответствующие категориям элементов. Сегментация основана по меньшей мере на одном из сегментации методом водораздела и сегментации контура тела, основанной на прилегающих слоях кожи и жира в изображении. Блок определения признаков определяет признаки по меньшей мере одного из a) областей изображения и b) границ между областями изображения, зависящими от значений изображения для изображения, и блок присваивания присваивает значения затухания областям изображения на основании определяемых признаков для формирования карты коррекции затухания. Технический результат: обеспечение возможности формирования высококачественной карты коррекции затухания. 3 ...

АВТОМАТИЗИРОВАННОЕ ОКОНТУРИВАНИЕ АНАТОМИИ ДЛЯ ПЛАНИРОВАНИЯ ТЕРАПИИ С УПРАВЛЕНИЕМ ПО ИЗОБРАЖЕНИЯМ

Изобретение относится к компьютерным системам диагностической визуализации. Техническим результатом является повышение точности распознания анатомических особенностей на изображении за счет автоматизированного оконтуривания этих особенностей. Предложена система оконтуривания анатомических особенностей на изображениях, используемых для планирования терапии с управлением по изображениям. Система включает в себя процессор, который принимает исходное изображение анатомической структуры пациента из устройства формирования изображений. Упомянутый процессор также обнаруживает анатомические опознавательные точки в исходном изображении и сравнивает позиции обнаруженных анатомических опознавательных точек с опорными опознавательными точками в опорном контуре, соответствующем анатомической структуре. А также процессор осуществляет сопоставление обнаруженных анатомических опознавательных точек с опорными опознавательными точками. 5 н. и 15 з.п. ф-лы, 5 ил.

СИСТЕМА И СПОСОБ ДОПОЛНЕНИЯ ИЗОБРАЖЕНИЯ СТИЛИЗОВАННЫМИ СВОЙСТВАМИ

Изобретение относится к процессу обработки изображения. Техническим результатом является расширение арсенала технических средств для дополнения объекта цифрового изображения стилизованными графическими свойствами. В способе дополнения объекта цифрового изображения стилизованными графическими свойствами идентифицируют на первом изображении первую версию объекта, обладающую первым набором графических свойств. Идентифицируют на втором изображении вторую версию объекта, обладающую вторым набором графических свойств. Извлекают первый и второй наборы графических свойств из первого и второго изображений. Создают третий набор графических свойств путем вычисления различий между первым и вторым наборами графических свойств. Используя третий набор графических свойств, дополняют первую версию объекта на первом изображении. 3 н. и 17 з.п. ф-лы, 8 ил., 1 табл.

СИСТЕМА ПОИСКА НАРУШЕНИЙ В ПОРЯДКЕ РАСПОЛОЖЕНИЯ ОБЪЕКТОВ

Изобретение относится к вычислительной технике. Техническим результатом изобретения является поиск нарушений в порядке расположения объектов с улучшенной функциональностью и большей точностью. Система поиска нарушений в порядке расположения объектов содержит: телекамеру; блок формирования обобщенных признаков области для разделения изображения на области и формирования для каждой области изображения многомерного цифрового вектора; блок формирования пространства признаков для формирования с помощью нейронной сети пространства признаков объектов для всего изображения в виде тензора признаков; блок анализа упорядоченности для анализа упорядоченности расположения объектов на изображении путем осуществления кластерного анализа тензора признаков с помощью многослойных нейронных сетей, при этом формирования карты связанных компонент в двумерном пространстве признаков объектов; блок принятия решений для определения нарушения порядка расположения объектов на изображении, при этом анализ карты связанных ...

СПОСОБ И УСТРОЙСТВО ДЛЯ АВТОМАТИЧЕСКОЙ СЕГМЕНТАЦИИ С УЛУЧШЕННОЙ ВОЗМОЖНОСТЬЮ ОБУЧЕНИЯ, ОСНОВАННОЙ НА АТЛАСАХ

УСТРОЙСТВО ДЛЯ СОЗДАНИЯ ПРИСВАИВАНИЙ МЕЖДУ ОБЛАСТЯМИ ИЗОБРАЖЕНИЯ И КАТЕГОРИЯМИ ЭЛЕМЕНТОВ

... 1. Устройство для создания присваиваний между областями изображения для изображения объекта и категориями элементов, причем устройство содержит:- блок (2, 30) предоставления изображения объекта для предоставления изображения времяпролетной позитронно-эмиссионной томографии без коррекции затухания в качестве изображения объекта для объекта (7),- блок (10) дифференцирования для дифференцирования изображения объекта,- блок (11) сегментирования для сегментирования изображения объекта на области изображения, применяя сегментирование методом водораздела,- блок (12) определения признака для определения признаков, по меньшей мере, одного из a) областей изображения и b) границ между областями изображения в зависимости от значений изображения для изображения объекта,- блок (5, 6, 31) предоставления предварительного изображения элементов для предоставления предварительного изображения элементов, содержащего первые предварительные присваивания между областями предварительного изображения и категориями ...

Ein Robotersystem mit automatisiertem Paketregistrierungsmechanismus und Erkennung geeigneter Mindestbereiche

Die vorliegende Offenbarung betrifft Verfahren und Systeme zum Erzeugen eines verifizierten geeigneten Mindestbereichs (MVR) eines Objektes. Eine exponierte Außenecke und exponierte Kanten eines Objektes können durch Verarbeiten von Bilddaten erkannt werden. Ein anfänglicher MVR kann durch Erkennen gegenüberliegender paralleler Kanten erzeugt werden, die den exponierten Kanten gegenüberliegen. Der anfängliche MVR kann angepasst werden und das angepasste Ergebnis kann getestet werden, um einen verifizierten MVR zu erzeugen.

Verfahren zum Darstellen einer ersten Struktur einer Körperregion mittels digitaler Subtraktionsangiographie, Auswerteeinrichtung sowie Angiographiesystem

Verfahren zum Darstellen einer ersten Struktur (9) einer Körperregion (8) mittels digitaler Subtraktionsangiographie mit folgenden Schritten: a) Empfangen von zumindest einem, mittels einer Angiographievorrichtung (2) erzeugten Füllbild (I2, I3) der Körperregion (8), welches eine zweite Struktur (10) der Körperregion (8) und die erste Struktur (9) bei einer ersten Kontrastmittelkonzentration in der ersten Struktur (9) darstellt (V1), wobei ein mittels der Angiographievorrichtung (2) ohne ein Kontrastmittel in der ersten Struktur (9) erzeugtes Leerbild (I1) empfangen wird; b) Bestimmen eines, die zweite Struktur (10) darstellenden Maskenbildes (M) der Körperregion (8) (V4), wobei das Maskenbild (M) anhand des Leerbildes (I1) und des zumindest einen Füllbildes (I2, I3) bestimmt wird, und wobei zum Bestimmen des Maskenbildes (M) das Leerbild (I1) und das zumindest eine Füllbild (I2, I3) mittels einer von dem Leerbild (I1) abhängigen Gewichtungsfunktion zeitlich gemittelt werden; c) Bestimmen ...

SYSTEM UND VERFAHREN ZUR OBJEKTERKENNUNG AUF DER GRUNDLAGE VON BILDDATEN

Es werden ein System und ein Verfahren zur Erkennung von Objekten bereitgestellt. Das Verfahren umfasst das Erzeugen einer ersten Kanteninformation aus ersten Bilddaten, die ein Objekt repräsentieren, auf der Grundlage eines ersten Modus der Bilderfassung, das Erzeugen einer zweiten Kanteninformation aus zweiten Bilddaten, die das Objekt repräsentieren, auf der Grundlage eines zweiten Modus der Bilderfassung, wobei sich der zweite Modus von dem ersten Modus unterscheidet, das Fusionieren der ersten Kanteninformation mit der zweiten Kanteninformation, um eine fusionierte Kanteninformation zu erzeugen, das Erzeugen einer Objekterkennungshypothese auf der Grundlage der fusionierten Kanteninformation und das Validieren der Objekterkennungshypothese auf der Grundlage der fusionierten Kanteninformation, der ersten Kanteninformation und/oder der zweiten Ka nteni nformation.

Verfahren zur Optimierung von angiographischen MR-Bildern

Die Erfindung betrifft ein Verfahren zur Optimierung von angiographischen Magnetresonanzbildern eines Untersuchungsobjekts, wobei bei den angiographischen Magnetresonanzbildern Arterien getrennt von den Venen darstellbar sind, mit den folgenden Schritten: - Aufnehmen von mehreren MR-Übersichtsbildern, wobei mindestens ein Bildgebungsparameter bei den Aufnahmen der MR-Übersichtsbilder variiert wird, - automatisches Berechnen eines optimierten Bildgebungsparameters anhand eines Qualitätskriteriums und - Bereitstellen des optimierten Bildgebungsparameters für die Aufnahme der angiographischen Magnetresonanzbilder, bei denen Arterien getrennt von den Venen darstellbar sind.

VERFOLGUNG SEMANTISCHER OBJEKTE IN VEKTORBILDSEQUENZEN

Controlling smoothness of a transition between images

A method of compositing or blending a first image and a second image comprising determining a difference of colour values between the first and second image along a boundary or edge associated with a position at which to composite the first image relative the second image 1003. An initial set of pixel data for each pixel within a region including the boundary is generated 1006, the pixel data relating at least one colour difference components indicating a difference in colour of the pixel between the first image and second image, and a colour difference indicator indicating that a difference of colour value exists. The pixel data is then recursively down-sampled until an extent of the pixels correspond with positive colour difference indicators 1009. The last down sampled image is then iteratively up-sampled to generate a set of final colour difference values 1012.

Method, apparatus and computer program product for filtering of media cintent

Detecting rapid changes in illuminance using angular differences between vectors in a YUV colour space

During post-processing of video data in a YUV color space it may be necessary, for instance for immersive video conferences, to separate a video object in the image foreground from the known image background. Hitherto, rapid, locally limited deviations in illumination in the actual image to be examined, in particular shadows and brightenings, could not be compensated for. The inventive recognition and compensation method of the present invention, however, can compensate for shadows and brightenings in real time, even for great quantities of image data, by directly utilizing different properties of the technically based YUV color space. Chrominance, color saturation and color intensity of an actual pixel (P1) are approximated directly from associated YUV values ( V , a, b) which leads to the avoidance of time-consuming calculations. The recognition of rapid deviations in illumination carried out in the YUV color space is based upon the approximation of a chrominance difference by an angle ...

Surface and image intergration for model evaluation and landmark determination

Image reconstruction system and method

A method and system for image reconstruction are provided. A projection image of a projection object may be obtained (420). A processed projection image may be generated based on the projection image through one or more pre-process operations (430). A reconstructed image including an artifact may be reconstructed based on the processed projection image (440). The artifact may be a detector edge artifact, a projection object edge artifact, and a serrated artifact. The detector edge artifact, the projection object edge artifact, and the serrated artifact may be removed from the reconstructed image (450).

Method and system for processing image data utilizing deep neural network

Methods and systems for processing image data utilising Deep Neural Networks (DNNs) are disclosed. The method comprises receiving image data collected by one or more sensors mounted in a mobility device, such as a robot, unmanned automobile or unmanned aerial vehicle (UAV). The DNN extracts topological information from the image data and generates a tree-like graph based on said pictures (Fig. 2). The images may consist of optical, infrared, lidar, radar, depth or sonar data, and information from a structured light sensor. The geographical information may comprise feature maps generated by at least one layer of the DNN. The topological information may represent semantic categories of potential trajectories. The dendritic graph generation can include identifying the current position of the mobility device as a parent node, from which candidate child nodes are expanded, iterating the procedure (Fig.4). The DNN may be trained by using a loss function, comparing a predicted graph with ground ...

Determining the status of a vehicle

Data processing systems

Operating a data processing system that comprises a display 20 and display controller 10 operable to provide to the display with data in respect of images to be displayed. When an image is to be displayed, the display controller provides image data and image modification data to the display, and the display uses the image data and the image modification data when displaying the image. Preferably the image modification data may comprise depth data related to stereoscopic or three dimensional (3D) image data, or may comprise information indicative of a difference and/or a similarity between a region of the output surface and another region of an output surface. The difference/similarity information may be generated based on a comparison performed at the display controller. Preferably, the method compares a region of a left image 41 of a stereoscopic image pair to a region of the right-eye image 42. The controller may selectively compare image regions based on a compression rate or based on ...

Business discovery from imagery

Image processing apparatus, image processing method and image processing program

Method and system for processing image data utlizing deep neural network

DEVICE AND PROCEDURE FOR THE DIGITIZATION OF AN OBJECT

MECHANISM AND PROCEDURE FOR PRODUCING SALIENZKARTE OF A PICTURE

Image correction of a surgical endoscope video stream

A method of real-time image correction of a video stream from a surgical endoscope of a surgical robotic system. The video stream comprises a sequence of images. The method comprises: for an image in the sequence of images, identifying a plurality of regions, each region having a different range of values of at least one image property to another region; applying a mask to the image which applies a region-specific modification to each region, each region-specific modification modifying the at least one image property of that region; deriving data from the surgical robotic system; determining a relationship between features in the image from the derived data; modifying the mask for a predicted relationship between the features at the time of a subsequent image in the video stream; and applying the modified mask to the subsequent image in the video stream.

CONTROLLING SMOOTHNESS OF A TRANSITION BETWEEN IMAGES

OF THE INVENTION Embodiments described herein are directed to methods and systems for facilitating control of smoothness of transitions between images. In embodiments, a difference of color values of pixels between a foreground image and the background image are identified along a boundary associated with a location at which to paste the foreground image relative to the background image. Thereafter, recursive down sampling of a region of pixels within the boundary by a sampling factor is performed to produce a plurality of down sampled images having color difference indicators associated with each pixel of the down sampled images. Such color difference indicators indicate whether a difference of color value exists for the corresponding pixel. To effectuate a seamless transition, the color difference indicators are normalized in association with each recursively down sampled image.

Remote sensing image super-resolution based on multi-dictionary sparse representation with fractal classification

Image super resolution technology has a wide range of applications in military, medical and other aspects of important application prospects, but also faced with a variety of problems. This patent studies a super-resolution technology of single remote sensing image. Firstly, the input high-resolution remote sensing image is blurred and sampled down to obtain the corresponding low-resolution image. Secondly, the low-resolution of the image is divided into a few patches, and to assess each small patch of fractal dimension, and according to the value size of the fractal dimension, the image patches are divided into two categories, texture patches and smooth patches. Thirdly, using the existing trained dictionary pairs for low-resolution texture and smooth patches corresponding to high-resolution image reconstruction. Finally, high-resolution image is synthesized from these small patches of high resolution.

Image processing apparatus, image processing method, and program

Abstract An image processing apparatus includes a calculating unit, an edge-position detecting unit, an identifying unit, and a subject-region detecting unit. The calculating unit calculates, for each of plural color separation images, a gradient direction of an edge gradient and an edge intensity at each position on the basis of a color captured image. The plural color separation images result from separation of the color captured image into multiple color components. The color captured image includes an image of a subject captured with an imaging apparatus. The edge-position detecting unit detects edge positions in each of the plural color separation images, on the basis of the gradient directions of the edge gradients and the edge intensities calculated by the calculating unit. The identifying unit identifies, for each position, the gradient direction of the edge gradient and the edge intensity at the position in a color separation image among the plural color separation images that ...

Foreground detection using fractal dimensional measures

A technique for foreground determination includes analyzing pixels that are deemed to be changing between frames, and applying a filtration technique that is based on fractal methods. Implementations include applying a filter that is designed to eliminate structures of dimensionality less than unity while preserving those of dimensionality unity and greater. The technique may be performed in real-time and makes use of a variable threshold for foreground determination and image segmentation techniques.

Device and method for detecting a plant against a background

A device for detecting a plant against a background includes a means (10) for providing a plurality of different photographs of the plant leaf against the background, the photographs differing in that image points of the different photographs which relate to the same location of the plant leaf are illuminated with different levels of brightness, a means (12) for selecting such image points, from the different photographs, whose levels of brightness are within a predetermined range, an image point of a first photograph being selected for a first location of the plant leaf, and an image point of a second, different photograph being selected for a different location of the plant leaf so as to obtain a representation of the plant leaf against the background, said representation being composed of and/or merged from different photographs, and a means (14) for segmenting the composite photograph so as to obtain a segment representation comprising the plant leaf without the background or the background ...

Device and method for detecting a plant against a background

A device for detecting a plant against a background includes a means (10) for providing a plurality of different photographs of the plant leaf against the background, the photographs differing in that image points of the different photographs which relate to the same location of the plant leaf are illuminated with different levels of brightness, a means (12) for selecting such image points, from the different photographs, whose levels of brightness are within a predetermined range, an image point of a first photograph being selected for a first location of the plant leaf, and an image point of a second, different photograph being selected for a different location of the plant leaf so as to obtain a representation of the plant leaf against the background, said representation being composed of and/or merged from different photographs, and a means (14) for segmenting the composite photograph so as to obtain a segment representation comprising the plant leaf without the background or the background ...

Intravascular imaging system interfaces and stent detection methods

The disclosure relates, in part, to computer-based visualization of stent position within a blood vessel. A stent can be visualized using intravascular data and subsequently displayed as stent struts or portions of a stent as a part of a one or more graphic user interface(s) (GUI). In one embodiment, the method includes steps to distinguish stented region(s) from background noise using an amalgamation of angular stent strut information for a given neighborhood of frames. The GUI can include views of a blood vessel generated using distance measurements and demarcating the actual stented region(s), which provides visualization of the stented region. The disclosure also relates to display of intravascular diagnostic information such as indicators. An indicator can be generated and displayed with images generated using an intravascular data collection system. The indicators can include one or more viewable graphical elements suitable for indicating diagnostic information such as stent information ...

Method of classification of organs from a tomographic image

The present invention relates to a method for classification of an organ in a tomographic image. The method comprises the steps of receiving (102) a 3-dimensional anatomical tomographic target image comprising a water image data set and a fat image data set, each with a plurality of volume elements, providing (104) a prototype image comprising a 3-dimensional image data set with a plurality of volume elements, wherein a sub-set of the volume elements are given an organ label, transforming (106) the prototype image by applying a deformation field onto the volume elements of the prototype image such that each labeled volume element for a current organ is determined to be equivalent to a location for a volume element in a corresponding organ in the target image, and transferring (108) the labels of the labeled volume elements of the prototype image to corresponding volume elements of the target image.

A METHOD SYSTEM AND ASSOCIATED MODULES AND SOFTWARE COMPONENTS FOR PROVIDING IMAGE SENSOR BASED HUMAN MACHINE INTERFACING

Disclosed is an image based human machine interface which comprises an image acquisition assembly which may acquire one or more of two-dimensional images of a user, wherein substantially each image may be associated with a different point in time. The human machine interface further comprises a processing unit which may classify one or more groups of pixels as either background or foreground.

CROP TYPE CLASSIFICATION IN IMAGES

In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations.

COMPUTER-IMPLEMENTED COMPOSITE TISSUE IMAGE WITH REAL-TIME ADJUSTABLE INTERFACE

The disclosure relates to devices, systems and methods for generating a digital image of a tissue section that is a composite of two or more source digital images of adjacent tissue sections and which may have a real-time adjustable boundary between different source images. The devices include computer software products for a fused-view visualization tool which permits one or more of generating and displaying the composite image and modifying the location of one or more boundaries between source images comprising the composite image. The systems include computer-implemented systems such as work stations and networked computers for analyzing tissue samples using the fused-view visualization tool. The methods include processes for visualization of a tissue sample as a composite image derived from two or more slides of adjacent tissue sections, for example as an interactive composite image wherein the proportion of each source image in the composite image may be altered.

METHOD AND APPARATUS FOR THREE DIMENSIONAL RECONSTRUCTION OF A JOINT USING ULTRASOUND

A method of generating a 3-D patient-specific musculoskeletal model is provided. The method acquires a plurality of raw radiofrequency ("RF") signals from an ultrasound scan of a patient's bone at a plurality of locations using an ultrasound probe that comprises a transducer array. The raw RF signals are tracked in 3-D space. Each raw RF signal is transformed into an envelope comprising a plurality of peaks by applying an envelope detection algorithm to each RF signal, each peak corresponding with a tissue interface echo. A bone echo is identified from the tissue interface echoes by selecting the last peak having a normalized envelope amplitude above a preset threshold. 2-D bone contours are determined from the plurality of bone echoes and then transformed into an integrated 3-D point cloud using the tracking data. A non-patient specific 3-0 bone model is transformed to generate the 3-D patient-specific bone model.

Metodo per la segmentazione di una griglia 3D discreta.

L'invenzione riguarda un metodo per l'identificazione delle regioni di confine tra diversi oggetti e genera automaticamente i contorni delle superfici all'interno dei dati scansionati. Il metodo qui descritto comprende le fasi di calcolo di un vettore normale in ogni punto di dati, la preselezione di punti in prossimità delle superfici in caso di dati affetti da rumore, l'identificazione delle diverse superfici e la limitazione di queste superfici a trovarsi nel punto di maggior contrasto. L'appplicazione principale è per l'imaging medico.

METHOD OF DETERMINING GEOMETRICAL PARAMETERS AND/OR CONDITION OF MATERIAL OF EXAMINED OBJECT BY MEANS OF RADIOGRAPHY

Method, device and system for remote deep learning of microscopic image reconstruction and segmentation

ANALYSIS OF A MULTISPECTRAL IMAGE

METHOD FOR DETERMINING THE OFFSET BETWEEN CENTRAL AND OPTICAL AXES OF AN ENDOSCOPE

La présente invention a pour objet un procédé de détermination du décalage ou désalignement entre l'axe médian ou de révolution et l'axe optique d'un endoscope rigide ou d'un dispositif similaire de prise de vues comprenant un corps rigide à enveloppe extérieure cylindrique profilée dans la direction de l'axe optique, ou comprenant au moins un segment d'extrémité rigide avec une telle enveloppe. Procédé caractérisé en ce qu'il consiste à effectuer, avec une caméra (3) faisant partie de l'endoscope (1), une pluralité de prises de vue avec un champ de vision limité par un contour, dont le positionnement par rapport à l'axe médian (A) est, pour chaque prise de vue, physiquement défini et spécifique, une rotation angulaire relative entre le contour et l'endoscope (1) intervenant entre deux prises de vues successives, et à déterminer un point ou un pixel dans les images successivement acquises dont la position demeure inchangée entre les différentes prises de vues, ce point correspondant à la ...

Image processing system with automatic extraction of semantic elements, uses repeated segmentation of image and identifies zones with high probability of object appearing, and uses weighted sum to determine object to be extracted

Selon le procédé, on soumet des images monoplan achromatiques corrélées à une pluralité d'opérations de segmentation en composants homogènes basés sur des critères différents, on analyse, pour chacune des images corrélées traitées, les résultats des différentes opérations de segmentation pour générer des noyaux constitués par des zones à forte certitude d'existence d'objets, on compare les noyaux résultants et on conserve les zones correspondant aux différents noyaux qui sont supérieures à un seuil déterminé comme significatives de la présence d'un objet, on procède à la sommation pondérée des images (111 à 113) précédemment traitées pour obtenir une première image résultat contenant les noyaux des composants homogènes, on effectue un coloriage puis une binarisation de la première image résultat pour obtenir une deuxième image résultat (112) contenant des composants homogènes, et on extrait les objets individualisés de l'image.

PROCESS Of IDENTIFICATION Of an ALVEOLAR NERVOUS ZONE IN an IMAGE OF JAW

Procédé d'identification d'une zone nerveuse alvéolaire dans une image de mâchoire, comprenant : (a) le découpage de l'image tridimensionnelle de mâchoire en tranches bidimensionnelles; (b) la détection d'un objet binaire correspondant à une zone de mâchoire depuis une tranche; (c) le rassemblement d'éléments d'image de l'objet en groupements d'éléments d'image d'intensité similaire; (d) la détermination de groupes ayant plus d'éléments d'image qu'un nombre minimal prédéterminé, et la détermination du groupe étiqueté minimal ayant la répartition d'intensité la plus faible; (e) la composition d'une nouvelle image binaire contenant des éléments d'image appartenant à la zone de mâchoire et aux groupes ayant une répartition d'intensité inférieure à celle du groupe minimal, pour extraire un objet nerveux candidat; et (f) la détermination si l'objet nerveux candidat correspond à la zone nerveuse alvéolaire réelle.

ULTRASOUND SYSTEM AND METHOD FOR PERFORMING VESSEL LABELING

VIDEO OBJECT ABSTRACTION APPARATUS AND ITS METHOD

FOREGROUND-BACKGROUND-AWARE ATROUS MULTISCALE NETWORK FOR DISPARITY ESTIMATION

Förfarande för att identifiera alveolärt nervområde i käkbild

SENSOR FUSION BASED PERCEPTUALLY ENHANCED SURROUND VIEW

Techniques including obtaining a first location of a vehicle, the vehicle having one or more cameras disposed about the vehicle, and wherein each camera is associated with a physical camera pose indicating where each camera is located with respect to the vehicle, capturing, by a first camera, a first image of a first area, associating the first image with the first location of the vehicle when the first image was captured, moving the vehicle in a direction so that the first area is no longer within a field of view of the first camera, obtaining a second location of the vehicle, determining a temporal camera pose based on the physical camera pose of the first camera and the second location of the vehicle, and rendering a view of the first area based on the temporal camera pose and the first image.

Inter-mode region-of-interest video object segmentation

The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or foreground objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or background areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Image inspection method

An image inspection method includes capturing a target object image, which the target object image comprises a plurality of graphical features; choosing a block image comprising a specific graphical feature of the plurality of graphical features from the target object image; capturing all the graphical features of the block image to obtain a region of interest (ROI); executing a filtering process or a recovering process on the ROI to obtain a pre-processed region; and inspecting, according to the pre-processed region, the target object image to determine whether the target object image has defects.

SYSTEMS AND METHODS FOR VISUALLY GUIDED AUDIO SEPARATION

A system for separating audio based on sound producing objects includes a processor configured to receive video data and audio data. The processor is also configured to perform object detection using the video data to identify a number of sound producing objects in the video data and predict a separation for each sound producing object detected in the video data. The processor is also configured to generate separated audio data for each sound producing object using the separation and the audio data.

METHOD, APPARATUS, AND MEDICAL IMAGING SYSTEM FOR SEGMENTING IMAGE OF OBJECT FROM IMAGE OF ORGAN

A method, an apparatus, a computer readable recording medium, and a medical imaging system are provided for segmenting an image of an object from an image of an organ. The method includes: generating a reference model of the object by using a priori knowledge related to the object of the organ; determining whether the first image includes a first area in which a shape of the object is unidentified; and in response to determining that the first image excludes the first area, segmenting a second image of the object from the first image, and in response to determining that the first image includes the first area, estimating a progression direction of the first area from the reference model to segment the second image from the first image.

Gesture detection method, gesture processing device, and computer readable storage medium

A gesture detection method, a gesture processing device, and a computer readable storage medium. The gesture detection method includes: performing a shrinking process and a background modeling on a first frame of original image, to generate a first frame of background image; determining a motion region of the first frame of background image based on the first frame of background image; determining a first gesture detection area in the first frame of original image according to the motion region of the first frame of background image; and performing a gesture detection within the first gesture detection area.

PARALLELISM IN DISPARITY MAP GENERATION

Input images are partitioned into non-overlapping segments perpendicular to a disparity dimension of the input images. Each segment includes a contiguous region of pixels spanning from a first edge to a second edge of the image, with the two edges parallel to the disparity dimension. In some aspects, contiguous input image segments are assigned in a “round robin” manner to a set of sub-images. Each pair of input images generates a corresponding pair of sub-image sets. Semi-global matching processes are then performed on pairs of corresponding sub-images generated from each input image. The SGM processes may be run in parallel, reducing an elapsed time to generate respective disparity sub-maps. The disparity sub-maps are then combined to provide a single disparity map of equivalent size to the original two input images.

PREDICTION APPARATUS, PREDICTION METHOD, PREDICTION PROGRAM

A prediction apparatus includes a learning section that performs machine learning in which, with respect to a combination of different types of captured images obtained by imaging the same subject, one captured image is set to an input and another captured image is set to an output to generate a prediction model; and a controller that performs a control for inputting a first image to the prediction model as an input captured image and outputting a predicted second image that is a captured image having a type different from that of the input captured image.

Hand-held medical-data capture-device having optical detection of vital signs from multiple filters and interoperation with electronic medical record systems via an authenticated communication channel

In one implementation, an apparatus estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point is described, estimates temperature from a digital infrared sensor and determines vital signs from a solid-state image transducer, or determines vital signs from a solid-state image transducer and estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point; after which the estimated and/or determined information is transmitted to an external database.

Systems and methods for image-based object modeling using multiple image acquisitions or reconstructions

Systems and methods are disclosed for integrating imaging data from multiple sources to create a single, accurate model of a patient's anatomy. One method includes receiving a representation of a target object for modeling; determining one or more first anatomical parameters of the target anatomical object from at least one of one or more first images of the target anatomical object; determining one or more second anatomical parameters of the target anatomical object from at least one of one or more second images of the target anatomical object; updating the one or more first anatomical parameters based at least on the one or more second anatomical parameters; and generating a model of the target anatomical object based on the updated first anatomical parameters.

IMAGE SEGMENTATION METHOD, APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM OF THE SAME

An image segmentation method is providing that includes the steps outlined below. A first image corresponding to a first time spot and a second image corresponding to a second time spot are received from a video stream, wherein the second time spot is behind the first time spot. Segmentation is performed on the second image by a segmentation neural network to generate a label probability set. Similarity determination is performed on the first image and the second image by a similarity calculation neural network to generate a similarity probability set. The label probability set and the similarity probability set are concatenated by a concatenating unit to generate a concatenated result. Further inference is performed on the concatenated result by a strategic neural network to generate a label mask.

CAMERA APPARATUS AND METHOD OF DETECTING A STREAM OF OBJECTS

A camera apparatus is provided for detecting a stream of objects moving relative to the camera apparatus having a plurality of individual cameras that each have an image sensor for recording frames, wherein the frames overlap one another in part, having an evaluation unit for compiling frames, and having a geometry detection sensor for detecting geometrical data of the objects. The evaluation unit is here configured to generate an object image assembled from frames of an individual object of the stream of objects, with the selection of the participating frames and/or the assembly taking place on the basis of the geometrical data.

System and method for segmentation of images of objects that are occluded by a semi-transparent material

An image processing system segments an object from the background of a scene where the scene is illuminated by unknown ambient light sources. An image of the scene with the object and an image of the background scene are captured by an image input device. These images are color corrected, using the image of a gray patch that is visible. A further transform converts the image from a red, green, blue format into a hue, saturation and intensity representation. The two images are then novelly compared on a pixel-by-pixel basis in the hue, saturation and intensity domain.

METHOD AND APPARATUS FOR CREATING A VISUAL MAP WITHOUT DYNAMIC CONTENT

Methods described herein relate to creating a visual map of an environment free of scene clutter. Methods may include: receiving sensor data from at least one image sensor, where the sensor data is representative of a plurality of images, each image representative of a scene at a scene location; processing each image using semantic scene segmentation to identify segments of the image of the scene; classifying the segments of each of the images of the scene into one of static elements or dynamic elements; generating a decluttered image of the scene, where the decluttered image includes only elements classified as static elements; providing for storage of the decluttered image of the scene in a database; and identifying a location of a device as the scene location in response to sensor data from the device corresponding to the decluttered image of the scene.

Method and Apparatus for Performing Segmentation of an Image

A method and system for segmenting a plurality of images. The method comprises the steps of segmenting the image through a novel clustering technique that is, generating a composite depth map including temporally stable segments of the image as well as segments in subsequent images that have changed. These changes may be determined by determining one or more differences between the temporally stable depth map and segments included in one or more subsequent frames. Thereafter, the portions of the one or more subsequent frames that include segments including changes from their corresponding segments in the temporally stable depth map are processed and are combined with the segments from the temporally stable depth map to compute their associated disparities in one or more subsequent frames. The images may include a pair of stereo images acquired through a stereo camera system at a substantially similar time.

SEGMENTATION METHOD OF MEDICAL IMAGE AND APPARATUS THEREOF

The present invention relates to a segmentation method and apparatus of a medical image. The segmentation method of a medical image according to an example of the present invention includes the steps of: extracting information about a position of a pointer according to a user input from a slice medical image displayed on a screen; determining a segmentation region including the position of the pointer, based on information about the slice medical image related to the extracted information about the position of the pointer; preliminarily displaying the determined segmentation region in the slice medical image; and when the preliminarily displayed segmentation region is affirmed by a user, determining the affirmed segmentation region as a lesion diagnosis region for the slice medical image.

ROBOTIC SYSTEM WITH AUTOMATED OBJECT DETECTION MECHANISM AND METHODS OF OPERATING THE SAME

A system and method for operating a robotic system to register unrecognized objects is disclosed. The robotic system may use first image data representative of an unrecognized object located at a start location to derive an initial minimum viable region (MVR) and to implement operations for initially displacing the unrecognized object. The robotic system may analyze second image data representative of the unrecognized object after the initial displacement operations to detect a condition representative of an accuracy of the initial MVR. The robotic system may register the initial MVR or an adjustment thereof based on the detected condition.

METHOD FOR GENERATING ROOF OUTLINES FROM LATERAL IMAGES

A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Image processing using measures of similarity

The invention provides methods of relating a plurality of images based on measures of similarity. The methods of the invention are useful in the segmentation of a sequence of colposcopic images of tissue, for example. The methods may be applied in the determination of tissue characteristics in acetowhitening testing of cervical tissue, for example.

Gas visualization arrangements, devices, and methods

Gas visualization in an image depicting a scene, for an example embodiment comprises capturing a first IR image depicting the scene at a first time instance and a second IR image depicting the scene at a second time instance; performing image processing operations on image data derived from said first IR image and from said second IR image, to generate a collection of data representing the location of gas in one of the first or second IR images; and generating a third image by adjusting pixel values in an image depicting the scene, dependent on pixel values of said collection of data. According to various embodiments, there is further provided further processing of the collection of data, and/or gas detection, before generation of the third image with adjusted pixel values.

Method of detecting edge under non-uniform lighting background

A method of determining an edge of an object on a digital image sequence comprising the step of determining a first gradient direction profile of a first image in the digital image sequence; determining a second gradient direction profile of a second image in the digital image sequence; computing a differential profile based on the first gradient direction profile and the second gradient direction profile; and determining the edge of the object based on the differential profile wherein the differential profile registers gradient magnitudes of the second gradient direction profile and angular differences between the first gradient direction profile and the second gradient direction profile. A system thereof is also disclosed.

Tracking semantic objects in vector image sequences

A semantic object tracking method tracks general semantic objects with multiple non-rigid motion, disconnected components and multiple colors throughout a vector image sequence. The method accurately tracks these general semantic objects by spatially segmenting image regions from a current frame and then classifying these regions as to which semantic object they originated from in the previous frame. To classify each region, the method performs a region based motion estimation between each spatially segmented region and the previous frame to compute the position of a predicted region in the previous frame. The method then classifies each region in the current frame as being part of a semantic object based on which semantic object in the previous frame contains the most overlapping points of the predicted region. Using this method, each region in the current image is tracked to one semantic object from the previous frame, with no gaps or overlaps. The method propagates few or no errors because ...

Utilizing machine learning models for inserting user-generated content into media content

A system may receive captured content identifying a first face of a user of a user device and media content identifying a second face of a person. The system may process the captured content and the media content, using a first machine learning model, to determine embeddings for the captured content and to identify landmarks in the media content. The system may combine the captured content with the media content, based on the landmarks and the embeddings, to generate combined content and to segment a combined face of the combined content, to replace the second face of the media content with the combined face, and to generate new media content that includes the combined face. The system may process the new media content, with a second machine learning model, to blend the combined face in the new media content and to generate final media content.

Computing apparatus and operation method of the same

Provided are a computing apparatus for constructing a mosaic image and an operation method of the same. The computing apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: segment an input image into a plurality of sub areas to obtain a plurality of sub area images, extract a feature from each of the plurality of sub area images, generate a plurality of source images respectively corresponding to the plurality of sub areas using an image generation neural network, the image generation neural network using, as a condition, the feature extracted from each of the plurality of sub area images, and combine the plurality of source images respectively corresponding to the plurality of sub areas to generate a mosaic image.

Image processing apparatus, image processing method, and storage medium

A virtual viewpoint foreground image generating unit generates a virtual viewpoint foreground image, which is an image of a foreground object seen from a virtual viewpoint without a shadow, based on received multi-viewpoint images and a received virtual viewpoint parameter. A virtual viewpoint background image generating unit generates a virtual viewpoint background image, which is an image of a background object seen from the virtual viewpoint, based on the received multi-viewpoint images and virtual viewpoint parameter. A shadow mask image generating unit generates shadow mask images from the received multi-viewpoint images. A shadow-added virtual viewpoint background image generating unit renders a shadow in the virtual viewpoint background image based on the received virtual viewpoint background image, shadow mask images, and virtual viewpoint parameter. A combined image generating unit generates a virtual viewpoint image by combining the virtual viewpoint foreground image with the ...

Intent-based dynamic change of region of interest of vehicle perception system

The present disclosure provides a perception system for a vehicle. The perception system includes a perception filter for determining a region of interest (“ROI”) for the vehicle based on an intent of the vehicle and a current state of the vehicle; and a perception module for perceiving an environment of the vehicle based on the ROI; wherein the vehicle is caused to take appropriate action based on the perceived environment, the current state of the vehicle, and the intent of the vehicle.

WEAKLY SUPERVISED IMAGE SEMANTIC SEGMENTATION METHOD, SYSTEM AND APPARATUS BASED ON INTRA-CLASS DISCRIMINATOR

A weakly supervised image semantic segmentation method based on an intra-class discriminator includes: constructing two levels of intra-class discriminators for each image-level class to determine whether pixels belonging to the image class belong to a target foreground or a background, and using weakly supervised data for training; generating a pixel-level image class label based on the two levels of intra-class discriminators, and generating and outputting a semantic segmentation result; and further training an image semantic segmentation module or network by using the label to obtain a final semantic segmentation model for an unlabeled input image. By means of the new method, intra-class image information implied in a feature code is fully mined, foreground and background pixels are accurately distinguished, and performance of a weakly supervised semantic segmentation model is significantly improved under the condition of only relying on an image-level annotation.

Multi-device vehicle intrusion detection

A vehicle theft-prevention apparatus can include at least one computing device coupled to at least one sensor and a wireless transceiver. The at least one sensor configured to sense measurements proximate to a vehicle. The at least one computing device can be configured to read a plurality of first measurements of the at least one sensor at a predetermined frequency, where the at least one sensor is located in a first position of the vehicle. The at least one computing device can be configured to receive a plurality of second measurements from at least one additional theft-prevention apparatus, where the at least one additional theft-prevention apparatus is located at a second position in the vehicle. The at least one computing device can determine that a person has entered the vehicle based on at least one of: the plurality of first measurements and the plurality of second measurements.

Image classification method, server, and computer-readable storage medium

An image classification method includes: obtaining an original image and a category of an object included in the original image; adjusting a first display parameter of the original image to obtain an adjusted original image; and transforming a second display parameter of the original image to obtain a new image. The adjusted first display parameter satisfies a value condition; and the transformed second parameter of the new image satisfies a distribution condition. The method also includes training a neural network model based on the category of the included object and a training set constructed by combining the adjusted original image and the new image; and inputting a to-be-predicted image into the trained neural network model, and determining the category of the object included in the to-be-predicted.

IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

An image processing apparatus comprises one or more processors, and one or more memories storing executable instructions which, when executed by the one or more processors, cause the image processing apparatus to function as: a specification unit configured to specify, on a basis of a difference between a first region, corresponding to a foreground region of a target frame, in a background image corresponding to the target frame in a buffered frame group and a second region, corresponding to the foreground region, in a background image of each of frames after the target frame in the buffered frame group, a background image among the background images of the frames; and an output unit configured to output the specified background image.

HEAT TRACE AREA EXTRACTION APPARATUS, HEAT TRACE AREA EXTRACTION METHOD AND PROGRAM

A heat trace region extraction device includes: a difference actual object image generation unit 130 that generates a difference actual object image which is an image of a difference between an actual object image which is an image of an actual object obtained by photographing a certain range with an actual object camera for photographing the actual object and a background actual object image which is an actual object image of a background of the certain range; a difference thermal image generation unit 16 that generates a difference thermal image which is an image of a difference between a thermal image which is an image of heat emitted by the actual object obtained by photographing the certain range with a thermal camera for photographing the heat emitted by the actual object and a background thermal image which is a thermal image of the background; and a heat trace region extraction unit 17 that extracts a heat trace region by removing a region of the actual object from the thermal image ...

HANDWASHING RECOGNITION SYSTEM AND HANDWASHING RECOGNITION METHOD

The present invention provides a method to prompt a user to perform appropriate handwashing based on the state of a hand or hand fingers of the user. A handwash monitoring system includes an imaging unit (10), a detector (21), and an abnormality type determination unit (22). The detector (21) detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging unit before handwashing, and detect a second candidate abnormality existing in the hand of the user from a second image captured by the imaging unit after the handwashing. The abnormality type determination unit (22) determines a type of an abnormality on the hand of the user based on a comparison between the first candidate abnormality and the second candidate abnormality.

PREDICTING A LIKELIHOOD THAT AN INDIVIDUAL HAS ONE OR MORE LESIONS

A mechanism for identifying the presence/absence of one or more lesions in a plurality of ultrasound images. The ultrasound images are processed to identify any regions of interest in each image, each region of interest representing a part of the ultrasound image suspected to depict a lesion. Similar regions of interest are grouped together, and the groups of similar regions are then processed using a machine-learning method to predict whether or not they contain a lesion of interest. Thus, a two-step process for identifying a lesion of interest is performed.

Способ предоперационного планирования пункционного трансфораминального внедискового эндоскопического доступа к позвоночному каналу поясничного отдела позвоночника

Изобретение относится к медицине, в частности к нейрохирургии, травматологии и ортопедии, лучевой диагностике. Способ включает в себя проведение магнитно-резонансной томографии позвоночника с получением сагиттального, полуаксиального и аксиального МР-срезов поясничного отдела, причем в зону сканирования включают кожные покровы спины. Определяют линейные и угловые параметры хирургического доступа. Для этого на сагиттальном МР-срезе строят две прямые линии, обозначая точки их пересечения с изогнутой линией, визуализирующей кожу спины. Первая прямая проходит через целевую точку, проецирующуюся в задне-верхнем углу тела позвонка нижележащего по отношению к целевому межпозвонковому диску, и вершину его верхнего суставного отростка. Вторая прямая линия является перпендикуляром к поверхности стола магнитно-резонансного томографа, проведенного через целевую точку. На полуаксиальном МР-срезе строят третью прямую линию, проходящую через точку, соответствующую целевой точке на сагиттальном МР-срезе ...

СИСТЕМЫ И СПОСОБЫ СЕГМЕНТАЦИИ ИЗОБРАЖЕНИЯ С ИСПОЛЬЗОВАНИЕМ СВЁРТОЧНОЙ НЕЙРОННОЙ СЕТИ

Настоящее изобретение относится к сегментации изображений. Технический результат заключается в улучшении точности автоматической сегментации изображений. В одной реализации предусмотрен реализуемый компьютером способ сегментации трёхмерного изображения. Способ может включать в себя прием трёхмерного изображения, полученного устройством формирования изображения, и создание первого стека двухмерных изображений из первой плоскости трёхмерного изображения и второго стека двухмерных изображений из второй плоскости трёхмерного изображения. Способ может дополнительно включать в себя сегментацию посредством процессора первого стека и второго стека двухмерных изображений с использованием по меньшей мере одной нейронно-сетевой модели. Способ может также включать в себя определение посредством процессора карты меток для трёхмерного изображения посредством агрегирования результатов сегментации из первого стека и второго стека. 3 н. и 27 з.п. ф-лы, 8 ил.

СИСТЕМА И МЕТОД ОБНАРУЖЕНИЯ ИЗНОСА И ПОТЕРИ ЗЕМЛЕРОЙНОЙ ТЕХНИКИ

Группа изобретений относится к системе и способу обнаружения износа техники во времени. Технический результат - сокращение погрешностей при обнаружении износа, минимизация влияния ложных срабатываний, уменьшение вычислительных ресурсов. Система обнаружения износа и потери землеройной техники содержит камеру, процессор, а также энергонезависимый машиночитаемый носитель информации, хранящий исполняемые инструкции. Исполняемые инструкции при исполнении процессором заставляют процессор выполнять операции, содержащие: получение с камеры видеопотока, включающего множество изображений ковша, имеющего по меньшей мере один инструмент для земляных работ; идентификацию множества изображений инструмента из видеопотока, причем множество изображений инструмента изображает инструмент для земляных работ в течение множества моментов времени за период времени; определение множества отсчетов пикселей инструмента из множества изображений инструмента; определение уровня износа или потери для по меньшей мере ...

СИСТЕМА И СПОСОБ ДОПОЛНЕНИЯ ИЗОБРАЖЕНИЯ СО СТИЛИЗОВАННЫМИ СВОЙСТВАМИ

СПОСОБ ФИЛЬТРАЦИИ ИЗОБРАЖЕНИЙ ЦЕЛЕВОГО ОБЪЕКТА В РОБОТЕХНИЧЕСКОЙ СИСТЕМЕ

... 1. Способ, включающий:захватывание объекта захватным устройством, прикрепленным к роботизированной руке;получение по меньшей мере двух исходных изображений области, содержащей объект, с помощью датчика изображения, в то время как объект перемещают посредством роботизированной руки;вычисление усредненного изображения для по меньшей мере двух исходных изображений;вычисление изображения расхождения для по меньшей мере двух исходных изображений;формирование фильтрующего изображения по изображению расхождения иполучение результирующего изображения путем маскирования усредненного изображения с использованием фильтрующего изображения в качестве битовой маски.2. Способ по п.1, также включающий:обнаружение успешного захватывания объекта в устройстве, связанном с захватным устройством и роботизированной рукой;в ответ на это обнаружение, выдачу инструкции датчику изображения на получение устройством по меньшей мере двух исходных изображений иполучение по меньшей мере двух исходных изображений устройством ...

СМЕШИВАНИЕ ИНФРАКРАСНОГО ОБЛАКА ТОЧЕК ДАННЫХ И ОБЛАКА ТОЧЕК ДАННЫХ ЦВЕТОВЫХ СОСТАВЛЯЮЩИХ

VORRICHTUNG UND VERFAHREN ZUR DIGITALISIERUNG EINES OBJEKTES

Ganzkörperbildaufnahme- und Bildverarbeitungssystem sowie Verfahren zu dessen Betrieb

Die Erfindung betrifft Verfahren zum Betrieb eines Ganzkörperbildaufnahme- und Bildverarbeitungssystems (02) zumindest umfassend die Verfahrensschritte: Aufnahme digitaler Bilder (B1B12) eines Untersuchungskörpers (04) anhand einer Mehrzahl vorgegebener Positionierungen des Untersuchungskörpers (04) durch ein Abbildungssystem (S31, S32); Erstellen von zumindest einem Kombinationsbild (B14, B58) aus zumindest zwei unterschiedlichen Bildern (B1B4, B5B8) der gleichen Positionierung des Untersuchungskörpers mittels einer Bildverarbeitungsvorrichtung (S41, S42); Identifizierung von Hautoberfläche (H) des Untersuchungskörpers in den Bildern und/oder Kombinationsbildern mittels der Bildbearbeitungsvorrichtung (S51, S52); Identifizieren vordefinierter Segmente der identifizierten Hautoberfläche (H) in den Bildern und/oder Kombinationsbildern mittels der Bildverarbeitungsvorrichtung (S6I, S6II); Identifizieren von Hautregionen in den Bildern und/oder Kombinationsbildern die Rötungen oder Schorfbildung ...

TIEFES LERNEN FÜR ENGMASCHIGE SEMANTISCHE SEGMENTIERUNG IN VIDEO MIT AUTOMATISIERTER INTERAKTIVITÄT UND VERBESSERTER ZEITLICHER KOHÄRENZ

Techniken, die sich auf automatisches Segmentieren von Videorahmen in pro Pixel engmaschige interessierende Objekte und Hintergrundbereiche beziehen, sind diskutiert. Solche Techniken enthalten das Anwenden eines neuronalen Segmentierungsfaltungsnetzes (Segmentierungs-CNN) auf eine CNN-Eingabe, die einen aktuellen Videorahmen, einen vorhergehenden Videorahmen, einen Kennzeichenrahmen des interessierenden Objekts, einen Bewegungsrahmen und mehrere Merkmalsrahmen, von denen jeder Merkmale enthält, die aus Merkmalsschichten eines neuronalen Objektklassifizierungsfaltungsnetzes wie es auf den aktuellen Videorahmen angewandt wird, komprimiert sind, enthält, um Kandidatensegmentierungen zu erzeugen, und Auswählen einer der Kandidatensegmentierungen als eine endgültige Segmentierung des aktuellen Videorahmens.

BEWEGUNGSANALYSESYSTEM UND EIN DIESES UMFASSENDES BEWEGUNGSVERFOLGUNGSSYSTEM VON BEWEGTEN ODER SICH BEWEGENDEN, SICH THERMISCH VON IHRER UMGEBUNG ABHEBENDEN OBJEKTEN

Segmentierungsnetzwerk

Segmentierungsnetzwerk (f) zum Vorhersagen von Ziellabels (Y) eines Zielansichtsbildes (X) einer Zieldomain mit Hilfe eines gelabelten Quellansichtsbilds (X) einer Quelldomain; wobei in der Quelldomain das Quellansichtbild (X) eine Szene in einem Quellblickwinkel abbildet und in der Zieldomain das Zielansichtbild (X) die Szene in einem von dem Quellblickwinkel unterschiedlichen Zielblickwinkel abbildet; trainiert durch die Schritte:Bestimmen (S10) einer Tiefenkarte (d) des Quellansichtbildes (X) zum Abschätzen einer Szenengeometrie der Szene durch ein Tiefenabschätzungsmodel (F);Bestimmen (S20) eines gewarpten Quellansichtsbildes (X) durch ein Warpen des Quellansichtbildes (X) in den Zielblickwinkel abhängig von der bestimmten Tiefenkarte (ds) durch einen differenzierbaren Renderingoperator (R) und Bestimmen von gewarpten Labels (Y) abhängig von bekannten Quelllabels (Y) des Quellansichtsbildes (X) und der bestimmten Tiefenkarte (d) durch den differenzierbaren Renderingoperator (R);Bestimmen ...

Display apparatus and display control method for displaying images

A display control unit 12 performs predetermined image processing 122 on at least one image data item among a plurality of pictures, and does not execute the pre-set image processing on at least another one image among the set of video items. A display unit 13 shows a number of image data items, if (A) it is determined, on the basis of metadata 125 associated with the image set, that the related images derive from the same apparatus, or if (B) that a presentation mode is set in which a number of images are to be depicted simultaneously, including a first image and a second picture which is a copy of the first one. The metadata may include information on the camera which has acquired the pictures (Fig, 7a). The image processing may comprise adding different colours according to the picture brightness level.

Image processing apparatus, image processing method, and image processing program

Identifying a Region of Interest in a Series ofMedical Images

A list of voxels of the series of images of the same subject at different time points is sorted according to a first variable, e.g. intensity, using for example an Iterative Connected Component (ICC) algorithm. A user selection of an initial voxel (510) in one image of the series of images is then registered. A region of interest (502) is then selected, the region being a plurality of voxels from the sorted list, the plurality comprising voxels from at least two images of the series, wherein each voxel of the plurality is either spatially or temporally adjacent in the series of images to another voxel of the plurality, and wherein voxels are selected for the region of interest according to a property in relation to the user-selected initial voxel.

Method for determining the extent of a foreground object in an image

Image foreground and background segmentation using aggregate relative pixel derivative

A method for determining, for example within a digital camera, whether a pixel belongs to a foreground or background segment within an image by evaluating derivative or deviation metrics (e.g. the ratio of derivate metric over deviation metric) in an area (block) around each pixel in the image, or ratios of derivative metrics across plural images, each captured at a different focus position. For each pixel, a block of pixels are examined to determine 406 an aggregate relative derivative (ARD), a block-based derivative; this derivative can be seen as a measure of defocus blur around an image location. The ARD is used, e.g. compared to a threshold value, to determine whether the pixel is to be assigned in the foreground segment or background segment. Multiple images may also be used to obtain ratios of a numerator of the ARD, to determine an extent of the foreground. The derivative metric may be determined from maximum and minimum derivatives for respective horizontal and vertical luminance ...

3D skeleton reconstruction from images using volumic probability data

Electronic Cleansing of Digital Data Sets

A method of processing a 3-dimensional Computed Tomography Colonography data set to remove tagged material is disclosed. The method involves the preliminary processing step of classifying voxels in the data set as corresponding to air, tagged material or colon tissue. Methods of overcoming erroneous classification of Partial Volume air/tagged material boundary voxels are disclosed. Specific tagged material voxels at the colon tissue/tagged material interface are isolated. The output of an edge recovery algorithm is recorded for the isolated tagged material voxels identified returning a value in excess of a threshold, to provide a plurality of edge recovered voxels. Each edge recovered voxel is subsequently reclassified as a colon tissue voxel and the tagged material voxels are deleted from the data set and reclassified as air.

System for background subtraction with 3d camera

A system for background image subtraction includes a computing device coupled with a 3D video camera, a processor of the device programmed to receive a video feed from the camera containing images of one or more subject that include depth information. The processor, for an image: segments pixels and corresponding depth information into three different regions including foreground (FG), background (BG), and unclear (UC); categorizes UC pixels as FG or BG using a function that considers the color and background history (BGH) information associated with the UC pixels and the color and BGH information associated with pixels near the UC pixels; examines the pixels marked as FG and applies temporal and spatial filters to smooth boundaries of the FG regions; constructs a new image by overlaying the FG regions on top of a new background; displays a video feed of the new image in a display device; and continually maintains the BGH.

Systems and methods for multilayer imaging and retinal injury analysis

Systems and methods are provided for imaging an eye and identifying retinal or subretinal features that may be indicative of pathologies such as macular degeneration and traumatic brain injury. An infrared or near-infrared image of an eye is interdependently smoothed and segmented and attributes of the image are determined. The attributes are indicative of retinal or subretinal features.

3-dimensional (3d) ultrasound system using image filtering and method for operating 3d ultrasound system

Provided are a 3-dimensional (3D) ultrasound system using image filtering and a method for operating the 3D ultrasound system. The 3D ultrasound system using image filtering may include a scanning unit to generate a volume image of an object, a detecting unit to detect a contour line in each of ‘n’ first slice images extracted from the volume image, ‘n’ being a natural number, and a rendering control unit to correct the ‘n’ first slice images into ‘n’ second slice images by removing a non-feature region based on the contour line, and to combine and render the ‘n’ second slice images.

Microscope, region determining method, and program

A microscope includes a dark-field illumination system that irradiates dark-field illumination light to a preparation in which a specimen is encapsulated between slide glass and cover glass by using an encapsulant, and an imaging unit that takes a dark-field image of the preparation irradiated with the dark-field illumination light. The microscope further includes a region determiner that detects the boundary between the encapsulant and air included between the slide glass and the cover glass based on the taken dark-field image and determines a region other than a region of the air as a region of interest for the specimen.

Method for detection of specimen region, apparatus for detection of specimen region, and program for detection of specimen region

A method for detecting the specimen region includes the first step for the first region detecting unit to detect the first region which is a region with contrast in the first image of an object for observation which is photographed under illumination with visible light, the second step for the second region detecting unit to detect the second region which is a region with contrast in the second image of the object for observation which is photographed under illumination with ultraviolet light, and the third step for the specimen region defining unit to define, based on the first and second regions mentioned above, the specimen region where there exists the specimen in the object for observation.

Method of ct angiography to visualize trans-osseous blood vessels

A method of CT angiography to visualize trans-osseous blood vessels is disclosed herein. This method for bone elimination and the visualization of trans-osseous blood vessels in two-scan (pre- and post-contrast) CT angiography is invented.

Automatic stereological analysis of biological tissue including section thickness determination

Systems and methods are provided for automatic determination of slice thickness of an image stack in a computerized stereology system, as well as automatic quantification of biological objects of interest within an identified slice of the image stack. Top and bottom boundaries of a slice can be identified by applying a thresholded focus function to determine just-out-of-focus focal planes. Objects within an identified slice can be quantified by performing a color processing segmentation followed by a gray-level processing segmentation. The two segmentation processes generate unique identifiers for features in an image that can then be used to produce a count of the features.

Plane-based Self-Calibration for Structure from Motion

Robust techniques for self-calibration of a moving camera observing a planar scene. Plane-based self-calibration techniques may take as input the homographies between images estimated from point correspondences and provide an estimate of the focal lengths of all the cameras. A plane-based self-calibration technique may be based on the enumeration of the inherently bounded space of the focal lengths. Each sample of the search space defines a plane in the 3D space and in turn produces a tentative Euclidean reconstruction of all the cameras that is then scored. The sample with the best score is chosen and the final focal lengths and camera motions are computed. Variations on this technique handle both constant focal length cases and varying focal length cases.

Method for the filtering of target object images in a robot system

The invention relates to a method and system for recognizing physical objects. In the method an object is gripped with a gripper, which is attached to a robot arm or mounted separately. Using an image sensor, a plurality of source images of an area comprising the object is captured while the object is moved with the robot arm. The camera is configured to move along the gripper, attached to the gripper or otherwise able to monitor the movement of the gripper. Moving image elements are extracted from the plurality of source images by computing a variance image from the source images and forming a filtering image from the variance image. A result image is obtained by using the filtering image as a bitmask. The result image is used for classifying the gripped object.

Video segmentation method

A system and method implemented as a software tool for foreground segmentation of video sequences in real-time, which uses two Competing 1-class Support Vector Machines (C-1SVMs) operating to separately identify background and foreground. A globalized, weighted optimizer may resolve unknown or boundary conditions following convergence of the C-1SVMs. The objective of foreground segmentation is to extract the desired foreground object from live input videos, with fuzzy boundaries captured by freely moving cameras. The present disclosure proposes the method of training and maintaining two competing classifiers, based on Competing 1-class Support Vector Machines (C-1SVMs), at each pixel location, which model local color distributions for foreground and background, respectively. By introducing novel acceleration techniques and exploiting the parallel structure of the algorithm (including reweighing and max-pooling of data), real-time processing speed is achieved for VGA-sized videos.

Video segmentation apparatus and method for controlling the same

A method for controlling a video segmentation apparatus is provided. The method includes receiving an image corresponding to a frame of a video; estimating a motion of an object in the received image to be extracted from the received image, determining a plurality of positions of windows corresponding to the object; adjusting at least one of a size and a spacing of at least one window located at a position of the plurality of determined positions of the windows based on an image characteristic; and extracting the object from the received image based on the at least one window of which the at least one of the size and the spacing is adjusted.

Apparatus and Method For Three-Dimensional Object Recognition

The present application relates to a method for recognising at least one object in a three-dimensional scene, the method including, in an electronic processing device: determining a plurality of two-dimensional images of the scene, the images at least partially including the at least one object; determining a plurality of two-dimensional segmentations of the at least one object, the two-dimensional segmentations corresponding to the two dimensional images; generating a three-dimensional representation of the scene using the images; generating a mapping indicative of a correspondence between the images and the representation; and using the mapping to map the plurality of segmentations to the three dimensional representation, to thereby recognise the at least one object in the scene. 1. A method for recognising at least one object in a three-dimensional scene , the method including , in an electronic processing device:determining a plurality of two-dimensional images of the scene, the images at least partially including the at least one object;determining a plurality of two-dimensional segmentations of the at least one object, the two-dimensional segmentations corresponding to the two dimensional images;generating a three-dimensional representation of the scene using the images;generating a mapping indicative of a correspondence between the images and the representation; andusing the mapping to map the plurality of segmentations to the three dimensional representation, to thereby recognise the at least one object in the scene.2. A method according to claim 1 , wherein the method includes claim 1 , in an electronic processing device:determining a pose estimation for each of the two-dimensional images;generating a plurality of two-dimensional representations of the three dimensional representation using the pose estimations, each two dimensional representation corresponding to a respective two dimensional image; and, generating the mapping using the two-dimensional ...

Hybrid Video Segmentation Aided by Optical Flow for Objects in Motion

Methods and systems including: applying a gamma adjustment to frames of an input video with particular values of parameters to generate at least one set of gamma-adjusted frames to generate at least one set of gamma-adjusted frames; applying a segmentation technique to the at least one set of gamma-adjusted frames to generate segmentation masks; applying an optical flow technique to the at least one set of the gamma-adjusted frames to generate optical flow maps; and combining the segmentation masks and the optical flow maps to generate hybrid segmentation masks. 1. A method , comprising:applying a gamma adjustment to frames of an input video with particular values of parameters to generate at least one set of gamma-adjusted frames;applying a segmentation technique to the at least one set of gamma-adjusted frames to generate segmentation masks;applying an optical flow technique to the at least one set of the gamma-adjusted frames to generate optical flow maps; andcombining the segmentation masks and the optical flow maps to generate hybrid segmentation masks.2. The method of claim 1 , wherein combining the segmentation masks and the optical flow maps comprisesmultiplying a pixel-wise code value for red, green, and blue components for sub-pixels of objects which have been applied with the segmentation technique.3. The method of claim 1 , wherein the segmentation technique comprises:categorizing each frame of the at least one set of gamma-adjusted frames into a class;detecting objects within each frame and drawing boundaries around the objects; andidentifying and corresponding parts of each frame to the objects.4. The method of claim 3 , wherein each flow map of the optical flow maps is a map of motion of the objects between consecutive frames of the at least one set of gamma-adjusted frames.5. The method of claim 1 , wherein the at least one set of gamma-adjusted frames comprises a first set of gamma-adjusted frames and a second set of gamma-adjusted frames.6. The ...

Automatic Crop Health Change Detection and Alerting System

A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

METHOD AND SYSTEM FOR IMAGE GENERATION

Systems and methods for generating a panorama image. Captured images are coarsely aligned, and then finely aligned based on a combination of constraint values. The panorama image is generated from the finely aligned images. 1. A method for generating a panorama image , comprising:with an image sensor of a mobile device, capturing video;with a display device of the mobile device, displaying the video in real-time;with the mobile device, performing guided image capture to automatically capture a set of captured images using the image sensor during display of the video;coarsely aligning the set of captured images in accordance with the guided image capture to generate a set of coarsely aligned images; dividing the image into one or more depth-stacked image layers,', 'dividing each image layer into a mesh of cells, defined by 2D cell vertices,', 'adjusting cell vertices to maximize a combination of constraint values including at least a feature constraint value and a global shape anchor constraint value, and', 'warping the image in accordance with the adjusted cell vertices to generate a finely warped image;, 'finely aligning pairs of images in the set of coarsely aligned images to generate a set of finely aligned images, comprising: for at least one image in the set of coarsely aligned imagesgenerating the panorama image by using the finely aligned images; andstoring the panorama image in a storage device.2. The method of claim 1 , wherein performing guided image capture comprises:superimposing a plurality of image centering targets onto the displayed video, each image centering target being associated with an image index;automatically capturing images of scenes that include a centered image centering target; andfor each captured image, assigning the image index of the centered image centering target to the image,wherein coarsely aligning the captured images comprises coarsely aligning the captured images in accordance with the assigned image indexes.3. The method of ...

Automated bone segmentation in images

A method for automated segmentation of a bone in an image dataset of a region of a human body, wherein said region comprises at least two adjacent bones, and wherein one of the bones comprises a shaft region and a head region, the method comprising identifying in the image dataset voxels belonging to a contiguous volume comprising both bones based on a density threshold; extracting a subset comprising the head region of the first bone, based on a boundary image slice; and applying a trained machine learning classifier to said subset, to generate a classification of each of voxel as belonging to one of the bones or a none-bone tissue.

Digital Image Processing System for Object Location and Facing

Despite the impressive advances made in recent decades, past digital image processing system were faced with significant technical challenges to solving important technical problems. The digital image processing system described below helps to solve these technical challenges with regard to spatial location and orientation of arbitrary objects in real-world environments. The digital image processing system performs image segmentation to accurately identify objects in an image, then locates the objects and determines their orientations.

SURFACE AND IMAGE INTEGRATION FOR MODEL EVALUATION AND LANDMARK DETERMINATION

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information. 136.-. (canceled)37. One or more non-transitory machine-readable storage media comprising a plurality of instructions stored thereon that , in response to execution by a computing device , causes the computing device to:process a three-dimensional volume image of a patient's joint to determine a border curve defined by an intersection of a plane and a three-dimensional surface model of a portion of the patient's joint; andgenerate an integrated image for displaying on a graphical interface, wherein the integrated image includes at least a portion of each of the three-dimensional volume image and the three-dimensional surface model and identifies the border curve.38. The one or more non-transitory machine-readable storage media of claim 37 , wherein the plurality of instructions further causes the computing device to create the three-dimensional surface model based on the three-dimensional volume image.39. The one or more non-transitory machine-readable storage media of claim 38 , wherein to create the three-dimensional surface model comprises:to generate the three-dimensional volume image from a set of magnetic resonance imaging (MRI) data slices; andto create the ...

SYSTEMS AND METHODS FOR IMAGE SEGMENTATION

The present disclosure may provide a method for segmenting an image. The method may include obtaining an image and related information. The image may include a tumor region. The method may also include determining a region of interest in the image. The region of interest may include the tumor region. The method may also include performing a first segmentation of the region of interest to obtain a first segmentation result. The first segmentation may include: determining tumor morphology relating to the tumor region; performing a second segmentation of the region of interest to obtain a second segmentation result; and optimizing, based on the tumor morphology, the second segmentation result to obtain the first segmentation result. 1. A method for segmenting an image implemented on at least one device each of which has at least one processor and at least one storage device , the method comprising:obtaining, by the at least one processor, an image and related information, the image including a tumor region;determining, by the at least one processor, a region of interest in the image, the region of interest including the tumor region; andperforming, by the at least one processor, a first segmentation of the region of interest to obtain a first segmentation result, wherein the first segmentation includes:determining tumor morphology relating to the tumor region;performing a second segmentation of the region of interest to obtain a second segmentation result; andoptimizing, based on the tumor morphology, the second segmentation result to obtain the first segmentation result.2. The method of claim 1 , the method further comprising:performing, based on the first segmentation result, a third segmentation of the region of interest.3. The method of claim 2 , wherein the third segmentation is based on a level-set algorithm.4. The method of claim 3 , wherein performing the third segmentation based on the level-set algorithm comprises:obtaining a distance field function; ...

Image segmentation and prediction of segmentation

Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

IMAGE RECOGNITION APPARATUS, LEARNING APPARATUS, IMAGE RECOGNITION METHOD, LEARNING METHOD, AND STORAGE MEDIUM

An apparatus includes an acquisition unit that acquires a first image based on a first parameter, and a second image based on a second parameter, a segmentation unit that segments each of the first and second images into a plurality of segments, an acquisition unit that acquires feature quantities from each of the plurality of segments formed by segmenting the first and second images, respectively, a calculation unit that calculates a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the first image, a classification unit that classifies the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability, and a determination unit that determines categories for the first and second fields based on the feature quantities acquired from the first and second images. 1. An apparatus comprising:an image acquisition unit configured to acquire a first image captured by an imaging unit based on a first parameter, and a second image captured by the imaging unit based on a second parameter;a segmentation unit configured to segment each of the first image and the second image into a plurality of segments;a feature quantity acquisition unit configured to acquire feature quantities from each of the plurality of segments formed by segmenting the first image and the second image, respectively;a calculation unit configured to calculate a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the plurality of segments of the first image;a classification unit configured to classify the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability; anda determination unit configured to determine categories for the first field and the second field based on the feature quantities acquired from ...

REAL-TIME IDENTIFICATION OF MOVING OBJECTS IN VIDEO IMAGES

The disclosed technology generally relates to detecting and identifying objects in digital images, and more particularly to detecting, identifying and/or tracking moving objects in video images using an artificial intelligence neural network configured for deep learning. In one aspect, a method comprises capturing a video input from a scene comprising one or more candidate moving objects using a video image-capturing device, where the video input comprises at least two temporally spaced image frames captured from the scene. The method additionally includes transforming the video input into one or more image pattern layers, where each of the image pattern layers comprises a pattern representing one of the candidate moving objects. The method additionally includes determining a probability of match between each of the image pattern layers and a stored image in a big data library. The method additionally includes adding one or more image pattern layers having the probability of match that exceeds a predetermined level to the big data library automatically, and outputting the probability of match to a user. 1. A method implemented on an electronic device for identifying a moving object in a video image using an artificial intelligence neural network configured for deep learning , the method comprising:capturing a video input from a scene comprising one or more candidate moving objects using a video image-capturing device, the video input comprising at least two temporally spaced image frames captured from the scene;transforming the video input into one or more image pattern layers, wherein each of the image pattern layers comprises a pattern representing one of the candidate moving objects;determining a probability of match between each of the image pattern layers and a stored image in a big data library;adding one or more image pattern layers having the probability of match that exceeds a predetermined level to the big data library automatically; andoutputting the ...

TIME DIVISION DISPLAY CONTROL METHODS AND APPARATUSES AND DISPLAY DEVICES

Embodiments of the present application disclose time division display control methods and apparatus and display devices, wherein a time division display control method disclosed comprises: displaying a first image by a display device; and changing display pixel distribution of the display device at least once within a preset permitted staying duration of vision of human eyes and displaying the first image by the display device changed each time, to cause the displayed first images to be displayed in human eyes as a second image in an overlapped manner. According to the present application, utilization of display pixels of a display device and a display quality of at least a local part of an image can be improved, thereby better meeting diversified actual application demands of users. 1. A time division display control method , comprising:displaying a first image by a display device; andchanging display pixel distribution of the display device at least once within a preset permitted staying duration of vision of human eyes and displaying the first image by the display device changed each time, to cause the displayed first images to be displayed in human eyes as a second image in an overlapped manner.2. The method of claim 1 , wherein the displaying the first image by the display device changed each time comprises:performing, according to actual location information of a display pixel in the changed display device, sampling processing on the first image that is to be displayed; andcontrolling the changed display device to display the first image on which the sampling processing is performed.3. The method of claim 1 , wherein the displaying the first image by the display device changed each time comprises:adjusting, according to actual location information of a display pixel in the changed display device, drive information of the first image that is to be displayed; andcontrolling the changed display device to display the corresponding first image according to the ...

IMAGE PROCESSING METHOD AND DEVICE

A first image to be processed is identified, where the first image includes one or more interference factors. The one or more interference factors are removed from the first image using a plurality of different interference factor removal techniques to obtain a plurality of sample images, where each of the plurality of sample images is associated with a particular interference factor removal technique. Each sample image of the plurality of sample images is segmented into a plurality of sample sub-images based on a segmentation rule, where each sample sub-image is associated with an attribute. A plurality of target sub-images is determined from the plurality of sample su b-images, where each target sub-image comprises a combination of sample sub-images associated with a common attribute, and where each target sub-image is associated with a different attribute. The plurality of target sub-images associated with different attributes is combined into a target image. 1. A computer-implemented method , comprising:identifying a first image to be processed, the first image including one or more interference factors;removing the one or more interference factors from the first image using a plurality of different interference factor removal techniques to obtain a plurality of sample images, wherein each of the plurality of sample images is associated with a particular interference factor removal technique;segmenting each sample image of the plurality of sample images into a plurality of sample sub-images based on a segmentation rule, wherein each sample sub-image is associated with an attribute;determining a plurality of target sub-images from the plurality of sample sub-images, wherein each target sub-image comprises a combination of sample sub-images associated with a common attribute, and wherein each target sub-image is associated with a different attribute; andcombining the plurality of target sub-images associated with different attributes into a target image.2. The ...

Image processing apparatus, control method, and non-transitory computer-readable storage medium

An image processing apparatus divides a first image into a plurality of areas based on obtained image data of the first image and sets relationship information between input brightness values with output brightness values for each of the plurality of areas, and converts, based on the relationship information, a brightness value of pixels included in the first image into a brightness value of pixels of a second image whose brightness range is narrower than that of the first image. In the conversion, the apparatus sets, for at least some pixels of the first image, a size of a window for specifying pixels within a predetermined area including the pixels based on environment information in which the second image is observed, and calculates a brightness value in the second image for each of the at least some pixels based on the window.

APPARATUS AND METHOD FOR PROVIDING 3-DIMENSIONAL AROUND VIEW

A three-dimensional around view providing apparatus for providing a 3D around view through a user interface module included in a vehicle may include a plurality of image pickup units mounted in the vehicle, a depth estimator configured to receive a plurality of images from the plurality of image pickup units and to acquire a plurality of depth maps corresponding to the plurality of images, a controller configured to minimize a depth difference between a first boundary region of a first depth map and a second boundary region of a second depth map. At least one among an autonomous vehicle, a user terminal, and a server according to embodiments of the present disclosure may be associated or integrated with an artificial intelligence (AI) module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a 5th-generation (5G) service related device, and the like. 1. A three-dimensional (3D) around view providing apparatus for providing a 3D around view through a user interface module included in a vehicle , the 3D around view providing apparatus comprising:a plurality of image pickup units mounted in the vehicle;a depth estimator configured to receive a plurality of images from the plurality of image pickup units and to acquire a plurality of depth maps corresponding to the plurality of images;an image processor configured to minimize a depth difference between a first boundary region of a first depth map and a second boundary region of a second depth map, to acquire a first around view depth map with the minimized depth difference between the first boundary region and the second boundary region, and to restore a 3D around view image for the plurality of images by using the first around view depth map, the first boundary region and the second boundary region being boundary regions in which the first depth map and the second depth map, which are adjacent to each other among the plurality of depth maps, overlap; anda ...

SYSTEMS AND METHODS FOR UROFLOWMETRY

A system for uroflowmetry is disclosed. The system can include an audio device configured to obtain audio data of urination. The system can include a machine learning module with at least one processor and associated memory, wherein the system is configured to: process the audio data of urination via a machine learning model that is trained based on simulated urine flow data and associated audio data received from a urine flow simulator, and real urine flow data and associated audio data received from a urine flow measuring device, and output the processed audio data of urination to be used in uroflowmetry. 1. A system for uroflowmetry , the system comprising:a microphone configured to obtain time-varying audio data of urination; and "process the audio data of urination via a machine learning model that is trained based on simulated urine flow data and associated audio data received from a urine flow simulator, and a human's real-time unaltered urine flow data received from a urine flow measuring device and the audio data of urination obtained from the microphone.", 'a processor and an associated memory, the processor is configured to2. The system of claim 1 , wherein the processor is configured to output the processed audio data of urination.3. The system of claim 1 , wherein the processor is configured to store the processed audio data of urination in the memory.4. The system of claim 1 , wherein the audio data of urination includes a sound recording of urine impacting a surface.5. The system of claim 1 , wherein the urine flow measuring device comprises:an imaging device configured to capture multiple image frames of urine flow; and apply an image segmentation algorithm on each of the image frames to obtain an image segment for each image frame, wherein the image segment includes image pixels associated with the urine flow, and', 'combine consecutive image segments to generate the real urine flow data., 'one or more processors and associated memory configured to6 ...

SURFACE AND IMAGE INTEGRATION FOR MODEL EVALUATION AND LANDMARK DETERMINATION

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information. 136.-. (canceled)37. A system for determining an accuracy of a bone model , the system comprising:at least one processor; and ["determine a landmark based on a user's selection of a point in a three-dimensional volume image of a patient's joint via a graphical interface,", 'identify a plane of the three-dimensional volume image that intersects the landmark by including the point,', "process the three-dimensional volume image to determine a border curve defined by an intersection of the plane and a three-dimensional surface model of a portion of the patient's joint, wherein the intersection divides the three-dimensional surface model into a first half surface and a second half surface, and", 'generate an integrated image for displaying on the graphical interface, wherein the integrated image includes at least a portion of the three-dimensional volume image, and wherein only the first half surface of the three-dimensional surface model and the integrated image identifies the border curve., 'at least one memory having a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to38. The system of claim 37 , ...

Method for mapping crystal orientations in a sample made of a polycrystalline material

The invention relates to a method for mapping the crystal orientations of a polycrystalline material, the method comprising: receiving ( 21 ) a series of images of the polycrystalline material, which images are acquired by an acquiring device in respective irradiation geometries; estimating ( 22 ) at least one intensity profile for at least one point of the material from the series of images, each intensity profile representing the intensity associated with the point in question as a function of irradiation geometry; and determining ( 24 ) a crystal orientation for each point in question of the material by comparing ( 23 ) the intensity profile associated with said point in question to theoretical signatures of intensity profiles of known crystal orientations, which signatures are contained in a database.

SYSTEMS AND METHODS FOR CONTOURING A SET OF MEDICAL IMAGES

Systems and methods are provided for contouring a set of medical images. Deformation field data is generated between a source image and a target image of the set of medical images. The deformation field data relates structures in the source image to corresponding structures in the target image and is generated in accordance with a deformable registration algorithm. The deformation field data is utilized to generate target contour data associated with the target image from source contour data, associated with the source image, that identifies one or more objects within the source image. 1. A method of contouring a three-dimensional (3D) medical image , comprising:receiving a source image slice, from a set of two-dimensional (2D) image slices constituting the 3D medical image, and source contour data identifying one or more objects within the source image slice;receiving a selection of a target image slice, from the set of the 2D image slices;generating deformation field data from the source image slice and the target image slice in accordance with a deformable registration algorithm, the deformation field data correlates locations in the source image slice to related locations in the target image slice; andgenerating target contour data that identifies the one or more objects within the target image slice based on the source contour data and the deformation field data.2. The method of claim 1 , further comprising generating contour data for a subset of the set of 2D image slices constituting the 3D image by:identifying a next target image slice in the set of 2D image slices to contour;generating new deformation field data correlating locations in the target image slice to related locations in the next target image slice; andapplying the new deformation field data to the target contour data to generate new target contour data that identifies the one or more objects within the next target image slice.3. The method of claim 2 , further comprising utilizing the next ...

AUTOMATIC DETECTION OF AN ARTIFACT IN PATIENT IMAGE DATA

A medical data processing method and system determines the position of an artifact in patient image data describing a set of tomographic slice images of an anatomical structure of a patient. The images are described by color Values. Color value difference data describing differences in color values for image elements in adjacent slice images is determined. At least one of positive or negative difference data, describing a subset of the differences and consisting of differences having a positive or negative value are determined. Smoothed difference data describing a smoothing of the differences contained in the positive or negative difference data are determined and, based on the positive or negative difference data and the smoothed difference data, artifact position data is determined describing the position of an artifact in the patient image data. 1. A method for determining the position of an artifact in patient image data , the method comprising:acquiring at an input of a medical data processing system comprising a memory device, a processor, and the input, patient image data, the patient image data describing a set of tomographic slice images of an anatomical structure of an associated patient, wherein the tomographic slice images of the patient image data are described by color values;determining by the processor of the medical data processing system, based on the patient image data and for each of a plurality of pairs of adjacent ones of the tomographic slice images, a corresponding plurality of color value difference data sets describing differences in color values of image elements between the adjacent ones of the tomographic slice images, each selected color value difference data set being determined as differences by subtracting, for a corresponding selected pair of adjacent tomographic slice images, a color value of an element of a first one of the selected pair of adjacent tomographic slice images from a color value of an element of a second one of the ...

Adaptive video subsampling for energy efficient object detection

Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.

Fully automated four-chamber segmentation of echocardiograms

Devices, systems and methods related to techniques for performing four-chamber segmentation of echocardiograms are disclosed. In one example aspect, a method for generating segmented image data based on an input echocardiogram includes receiving an input echocardiogram that includes information associated with four chambers of a heart, performing segmentation on the information associated with the four chambers using an adversarial model that comprises a first artificial neural network with multiple layers, and combining data from selected layers of the first artificial neural network to generate an output image that includes the segmented four chambers of the heart.

Method for determining the extent of a foreground object in an image

Embodiments are directed towards determining within a digital camera whether a pixel belongs to a foreground or background segment within a given image by evaluating a ratio of derivative and deviation metrics in an area around each pixel in the image, or ratios of derivative metrics across a plurality of images. For each pixel within the image, a block of pixels are examined to determine an aggregate relative derivative (ARD) in the block. The ARD is compared to a threshold value to determine whether the pixel is to be assigned in the foreground segment or the background segment. In one embodiment, a single image is used to determine the ARD and the pixel segmentation for that image. Multiple images may also be used to obtain ratios of a numerator of the ARD, useable to determine an extent of the foreground.

SYSTEMS AND METHODS FOR ANALYTICAL COMPARISON AND MONITORING OF ANEURYSMS

Systems and methods for identifying growth of a cerebral aneurysm. The method includes forming a first virtual skeleton model from a first image segmentation and forming a second virtual skeleton model from a second image segmentation. The virtual skeleton models including a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of a plurality of blood vessel surface points. The method includes identifying one or more second terminal points within the second virtual skeleton model and overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points. 1. A method for identifying growth of a cerebral aneurysm , the method comprising:providing a first image segmentation of a surface of a blood vessel network, the first image segmentation comprising a first plurality of blood vessel surface points;forming a first virtual skeleton model from the first image segmentation, the first virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of blood vessel surface points;identifying one or more first nodes within the first virtual skeleton model, each of the one or more first nodes intersecting more than two edges of the plurality of edges;providing a second image segmentation of a surface of the blood vessel network, the second image segmentation comprising a second plurality of blood vessel surface points;forming a second virtual skeleton model from the second image segmentation, the second virtual skeleton model comprising a plurality of edges, each edge of the plurality of edges having a plurality of skeleton points, each skeleton point of the plurality of skeleton points being associated with a subset of the plurality of ...

DYNAMIC STRUCTURED LIGHT FOR DEPTH SENSING SYSTEMS BASED ON CONTRAST IN A LOCAL AREA

A depth camera assembly (DCA) determines depth information. The DCA projects a dynamic structured light pattern into a local area and captures images including a portion of the dynamic structured light pattern. The DCA determines regions of interest in which it may be beneficial to increase or decrease an amount of texture added to the region of interest using the dynamic structured light pattern. For example, the DCA may identify the regions of interest based on contrast values calculated using a contrast algorithm, or based on the parameters received from a mapping server including a virtual model of the local area. The DCA may selectively increase or decrease an amount of texture added by the dynamic structured light pattern in portions of the local area. By selectively controlling portions of the dynamic structured light pattern, the DCA may decrease power consumption and/or increase the accuracy of depth sensing measurements. 1. A depth camera assembly (DCA) comprising:a structured light (SL) projector configured to project one or more SL patterns into a local area in accordance with illumination instructions;a camera assembly configured to capture images of a portion of the local area including the one or more SL patterns; and provide first illumination instructions to the SL projector, wherein the first illumination instructions cause the SL projector to project a first SL pattern having a first pattern element density into a first region of interest based on a first amount of contrast in the first region of interest; and', 'provide second illumination instructions to the SL projector, wherein the second illumination instructions cause the SL projector to project a second SL pattern having a second pattern element density into a second region of interest based on a second amount of contrast in the second region of interest, wherein the second amount of contrast is greater than the first amount of contrast., 'a controller configured to2. The DCA of claim 1 , ...

SYSTEM AND METHOD FOR SPLICING IMAGES

The present disclosure relates to systems and methods for image splicing. The systems and methods may acquire a first image and a second image, determine a plurality of first feature points in a first region of the first image, determine a plurality of second feature points in a second region of the second image, then match the plurality of first feature points with the plurality of second feature points to generate a plurality of point pairs. Based on the plurality of point pairs, a third region on the first image and a fourth region on the second image may be determined. Finally, a third image may be generated based on the first image and the second image, wherein the third region of the first image may overlap with the fourth region of the second image in the third image. 1. A system comprising:at least one storage including a set of instructions or programs;at least one processor configured to communicate with the at least one storage, wherein when executing the set of instructions or programs, the at least one processor is directed to:acquire a first image and a second image;determine a plurality of first feature points in a first region of the first image;determine a plurality of second feature points in a second region of the second image;match the plurality of first feature points with the plurality of second feature points to generate a plurality of point pairs;determine a third region on the first image and a fourth region on the second image based on the plurality of point pairs; andgenerate a third image based on the first image and the second image, wherein the third region of the first image overlaps with the fourth region of the second image in the third image.2. The system of claim 1 , wherein to determine a plurality of first feature points in a first region of the first image claim 1 , the at least one processor is further directed to:decompose the first image;generate a first difference image corresponding to the decomposed first image;generate a ...

Determination of an image series in dependence on a signature set

For the determination of an image series from a set of a number of image series, in each case of a signature is compiled for each of the number of image series. This signature is formed from a set of attributes of the respective image series. A signature from the signatures for the image series is ascertained that is most similar to a prespecified signature set. An action is performed with the image series whose signature was ascertained as the most similar signature.

IMAGE RECOGNITION METHOD, APPARATUS AND DEVICE, AND NON-VOLATILE COMPUTER STORAGE MEDIUM

The present disclosure provides an image recognition method and apparatus, a device and a non-volatile computer storage medium. In embodiments of the present disclosure, it is feasible to obtain the to-be-recognized image of the designated space, then perform image segmentation processing for the to-be-recognized image, to obtain at least one area image of the designated space, and then perform image matching processing for each area image in said at least one area image, to obtain a reference image corresponding to said each area image, so that it is possible to perform recognition processing for said each area image according to image information of the reference image corresponding to said each area image to obtain article information of said each area image. The so doing does not require manual participation and exhibits simple operations and a high rate of correctness, and thereby improves the recognition efficiency and reliability. 116-. (canceled)17. An image recognition method , wherein the method comprises:obtaining a to-be-recognized image of a designated space;performing image segmentation processing for the to-be-recognized image, to obtain at least one area image of the designated space;performing image matching processing for each area image in said at least one area image, to obtain a reference image corresponding to said each area image;performing recognition processing for said each area image according to image information of the reference image corresponding to said each area image, to obtain article information of said each area image.18. The method according to claim 17 , wherein the designated space comprises an internal space of storage equipment.19. The method according to claim 17 , wherein the performing image segmentation processing for the to-be-recognized image claim 17 , to obtain at least one area image of the designated space comprises:using an article detector to perform image segmentation processing for the to-be-recognized image, ...

Template Based Anatomical Segmentation of Medical Images

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations. The multi-atlas segmentation engine retrieves offline registrations corresponding to the subset of image templates from the first registration storage device. The multi-atlas segmentation engine performs segmentation of the target image based on the retrieved offline registrations corresponding to the subset of image templates and the plurality of online registrations. The segmentation applies labels to anatomical structures present in the target image based on the retrieved offline registrations and the plurality of online registrations to thereby output a modified target image. 1. A method , in a data processing system comprising a processor and a memory , the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine , the method comprising:performing, by an offline registration component executing within the multi-atlas segmentation engine, registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a ...

Template Based Anatomical Segmentation of Medical Images

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations. The multi-atlas segmentation engine retrieves offline registrations corresponding to the subset of image templates from the first registration storage device. The multi-atlas segmentation engine performs segmentation of the target image based on the retrieved offline registrations corresponding to the subset of image templates and the plurality of online registrations. The segmentation applies labels to anatomical structures present in the target image based on the retrieved offline registrations and the plurality of online registrations to thereby output a modified target image. 120-. (canceled)21. A method , in a data processing system comprising a processor and a memory , the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine , the method comprising:performing, by an offline registration component executing within the multi-atlas segmentation engine, high-resolution registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first ...

IMAGE SEGMENTATION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM

An image segmentation method is provided for a computer device. The method includes obtaining a plurality of sample images, calling an initial model to input the plurality of sample images into the initial model and to train the initial model based on the plurality of sample images to obtain an image segmentation model and, based on the initial model, determining a number of image segmentation modules according to a number of types of pixels of the plurality of sample images. Different image segmentation modules are used for segmenting different regions of an image. The method also includes calling the image segmentation model in response to obtaining a first image to be segmented, and segmenting the first image by using the image segmentation model based on a plurality of image segmentation modules, to output a second image. Computer device and non-transitory computer-readable storage medium counterparts are also contemplated. 1. An image segmentation method for a computer device , comprising:obtaining a plurality of sample images;calling an initial model to input the plurality of sample images into the initial model, and to train the initial model based on the plurality of sample images to obtain an image segmentation model;based on the initial model, determining a number of image segmentation modules according to a number of types of pixels of the plurality of sample images, wherein different image segmentation modules are used for segmenting different regions of an image; andcalling the image segmentation model in response to obtaining a first image to be segmented, and segmenting the first image by using the image segmentation model based on a plurality of image segmentation modules, to output a second image, wherein the plurality of sample images and the first image all are target human tissue images.2. The method according to claim 1 , wherein the determining a number of image segmentation modules according to a number of types of pixels of the plurality of ...

System and method of object detection based on image data

A system and method of detecting objects are provided. The method includes generating first edge information from first image data representing an object based on a first mode of image capture, generating second edge information from second image data representing the object based on a second mode of image capture, the second mode being different from the first mode, fusing the first edge information with the second edge information to generate fused edge information, generating an object detection hypothesis based on the fused edge information, and validating the object detection hypothesis based on the fused edge information, the first edge information, and/or the second edge information.

IMAGE PROCESSING APPARATUS, METHOD OF CONTROLLING IMAGE PROCESSING APPARATUS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

An image processing apparatus comprising, a first processing unit configured to process a first image stored in a first memory and output a first processing result in a first size, a conversion unit configured to, if the first size matches a second size, output the first processing result, and if the first size is different from the second size, convert the first processing result into the second size and output a result of the conversion, and a second processing unit configured to process the first processing result outputted from the conversion unit and a second image of the second size stored in a second memory, and to store a second processing result in the second memory in the second size. 1. An image processing apparatus comprising:a first processing unit configured to process a first image stored in a first memory and output a first processing result in a first size;a conversion unit configured to, if the first size matches a second size, output the first processing result, and if the first size is different from the second size, convert the first processing result into the second size and output a result of the conversion; anda second processing unit configured to process the first processing result outputted from the conversion unit and a second image of the second size stored in a second memory, and to store a second processing result in the second memory in the second size.2. The image processing apparatus according to claim 1 , wherein the conversion unit claim 1 ,if the first size is larger than the second size, reduces the first processing result to the second size and outputs a result of the reduction, and if the first size is smaller than the second size, enlarges the first processing result to the second size and outputs a result of the enlargement.3. The image processing apparatus according to claim 1 , wherein the conversion unit claim 1 ,if the first size is not larger than the second size, stores in a third memory the first processing result ...

SKIN ASSESSMENT USING IMAGE FUSION

Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a parallel-polarized image and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment. 1. An apparatus comprising:a storage device configured to store instructions; and obtain a parallel-polarized image of the skin area;', 'obtain a cross-polarized image of the skin area;', 'generate a texture map from a combination of the parallel-polarized and cross-polarized images;', 'generate at least one of a texture map image or a texture metric from the texture map; and', 'cause the at least one of the texture map, the texture map image, or the texture metric to be displayed, stored, communicated, or processed., 'a processor configured to execute instructions stored in the storage device to2. The apparatus of claim 1 , wherein the combination includes an intensity of the parallel-polarized image divided by an intensity of the cross-polarized image.3. The apparatus of claim 1 , wherein the processor is configured to execute instructions to perform an image registration operation on the parallel-polarized and cross-polarized images before generating the texture map.4. The apparatus of claim 1 , wherein the processor is configured to execute instructions to generate a flattened texture map.5. The apparatus of claim 1 , wherein generating the texture metric includes:applying a two-dimensional wavelet transform to the texture map to determine low- and high-frequency components, ...

INTERNAL THERMAL FAULT DIAGNOSIS METHOD OF OIL-IMMERSED TRANSFORMER BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK AND IMAGE SEGMENTATION

The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results. 1. An internal thermal fault diagnosis method of an oil-immersed transformer based on deep convolutional neural network and image segmentation , comprising the following steps:1) dividing an internal area of a transformer, and using a fault area and a normal status as a labels of a deep convolutional neural network;2) through lattice Boltzmann simulation, randomly obtaining a large number of feature images of an internal temperature field distribution of the oil-immersed transformer under normal and various fault state modes, and the fault area is used as the label to form an underlying training sample set;3) obtaining historical monitoring information of an infrared camera or a temperature sensor, and forming its corresponding fault diagnosis results into labels according to the division method of step 1);4) combining all the monitoring information contained in each sample in step 3) into one image, and then extracting the same monitoring information from the samples in the underlying training ...

Method and Device for Balancing Foreground-Background Luminosity

In one embodiment, a method includes: obtaining a first image of a scene while an illumination component is set to an inactive state; obtaining a second image of the scene while the illumination component is set to a pre-flash state; determining one or more illumination control parameters for the illumination component for a third image of the scene that satisfy a foreground-background balance criterion based on a function of the first and second images in order to discriminate foreground data from background data within the scene; and obtaining the third image of the scene while the illumination component is set to an active state in accordance with the one or more illumination control parameters. 1. A method comprising: obtaining, by the image sensor, a first image of a scene while the illumination component is set to an inactive state;', 'obtaining, by the image sensor, a second image of the scene while the illumination component is set to a pre-flash state;', 'determining, based on the first image and the second image, one or more illumination control parameters for the illumination component for a third image of the scene that satisfy a foreground luminosity criterion and a foreground-and-background luminosity criterion; and', 'obtaining, by the image sensor, the third image of the scene while the illumination component is set to an active state in accordance with the one or more illumination control parameters., 'at a device with one or more processors, non-transitory memory, an image sensor, and an illumination component2. The method of claim 1 , wherein determining the one or more illumination control parameters includes:determining a first macro luminance of the first image;determining a second macro luminance of the second image;determining a difference between the first macro luminance and the second macro luminance; anddetermining the one or more illumination control parameters based on the difference.3. The method of claim 2 , further comprising: ...

Method for displaying image data of body regions of a patient, the body regions being prone to dementia, and a medical imaging system which is designed to carry out the method

A method is disclosed for displaying image data of body regions of a patient using a medical imaging system including a first imaging apparatus and a positron emission tomography apparatus, the body regions being prone to dementia. The method includes provision of first image data recorded using the first imaging apparatus; provision of second image data recorded using the positron emission tomography apparatus, the first image data and the second image data being recorded simultaneously or at short intervals of time consecutively; segmentation of the first image data in respect of body regions prone to dementia, a segmentation mask being generated to this end on the basis of the first image data; generation of results data, the results data including a selection of voxels in the second image data made using the segmentation mask; and display of the results data.

SYSTEMS AND METHODS FOR SEGMENTING IMAGES

Embodiments disclosed herein relate to systems and methods for segmenting 2D images. Certain embodiments provide a method of segmenting a 2D image. The method includes obtaining a 2D mask for the 2D image, wherein the 2D mask comprises a portion of the 2D image. The method includes selecting one or more first voxels of the 2D image not part of the 2D mask and adding the one or more first voxels of the 2D image to the 2D mask. The method includes selecting one or more second voxels of the 2D image and performing a morphological closing on one or more third voxels comprising the one or more second voxels of the 2D image that are part of the 2D mask. The method includes performing an automatic flood-fill operation on at least a portion of the 2D mask. 1. A method for using a cursor to perform different operations on different data , comprising:selecting first pixels and second pixels of in image using a cursor comprising a first region and a second region, respectively;performing a first operation on data corresponding to the first pixels; andperforming a second operation, different than the first operation, on data corresponding to the second pixels.2. The method of claim 1 , wherein the first region corresponds to a portion of the second region.3. The method of claim 1 , wherein the first region is one of a circle claim 1 , rectangle and square.4. The method of claim 1 , wherein the second region is one of a circle claim 1 , rectangle and square.5. The method of claim 1 , wherein the first and second regions have a same shape.6. The method of claim 1 , wherein the first and second regions have a different shape.7. The method of claim 1 , wherein selecting the first and second pixels comprises one or more of clicking claim 1 , holding claim 1 , or dragging the cursor over the image.8. The method of claim 1 , wherein the image is one of a plurality of 2D images forming a stack claim 1 , the plurality of 2D images corresponding to a 3D representation of an object.9. The ...

Method for analyzing an image of a dental arch

A method for enriching a learning base intended for the training of a deep learning device. Production of an updated reference model of a dental arch and segmentation to produce a tooth model for each tooth. Acquisition of at least one updated image of the arch. For each updated image, search for virtual acquisition conditions suitable for an acquisition of a reference image of the updated reference model exhibiting a maximum match with the updated image, and acquisition of the reference image. Identification, in the reference image, of at least one reference tooth zone representing a tooth model by comparison of the updated image and the reference image determination of an updated tooth zone representing the tooth model. Assignment, to the updated tooth zone, of the tooth attribute value or values of the tooth model. Addition of the updated image enriched with value or values in the learning base.

Method and Device for Generating an Unmanned Aerial Vehicle Flight Trajectory, Computer Apparatus and Storage Medium

A method for generating a UAV flight trajectory. The method includes acquiring map data of a to-be-reconstructed area; the map data includes a satellite map image and a plane map image; acquiring building silhouettes of each building in a to-be-reconstructed area from a plane map image; acquiring height data of each building from a satellite map image; generating an initial model according to the building silhouettes and height data of each building; determining a view set according to an initial model; generating a flight trajectory from a view set. The initial model can be established without pre-flight of the to-be-reconstructed area, and the UAV flight trajectory can be directly generated, so that the calculation amount of data is reduced. 1. A method for generating an unmanned aerial vehicle (UAV) flight trajectory , comprising:acquiring map data of a to-be-reconstructed area, the map data comprising a satellite map image and a plane map image;acquiring building silhouettes corresponding to each of buildings in the to-be-reconstructed area according to the plane map image;acquiring height data corresponding to each of the buildings according to the satellite map image;generating an initial model according to the building silhouettes and the height data corresponding to each of the buildings;determining a view set according to the initial model, the view set comprising a plurality of views when a UAV photographs the to-be-reconstructed area; andgenerating a flight trajectory according to the view set.2. The method according to claim 1 , wherein the determining the view set according to the initial model comprises:acquiring a viewing distance, a field of view, and a view overlapping rate, and generating a Poisson sampling diameter;serving the initial model and the Poisson sampling diameter as inputs, generating a sampling point set by a Poisson distribution sampling algorithm, the sampling point set comprising a plurality of sampling points, each of the sampling ...

DEEP LEARNING FOR DENSE SEMANTIC SEGMENTATION IN VIDEO WITH AUTOMATED INTERACTIVITY AND IMPROVED TEMPORAL COHERENCE

Techniques related to automatically segmenting video frames into per pixel dense object of interest and background regions are discussed. Such techniques include applying a segmentation convolutional neural network (CNN) to a CNN input including a current video frame, a previous video frame, an object of interest indicator frame, a motion frame, and multiple feature frames each including features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame to generate candidate segmentations and selecting one of the candidate segmentations as a final segmentation of the current video frame. 1. A system for providing segmentation in video comprising:a memory to store a current video frame; and generate a convolutional neural network input comprising the current video frame, a temporally previous video frame, an object of interest indicator frame comprising one or more indicators of an object of interest in the current video frame, a motion frame comprising motion indicators indicative of motion from the previous video frame to the current video frame, and a plurality of feature frames each comprising features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame;', 'apply a segmentation convolutional neural network to the convolutional neural network input to generate a plurality of candidate segmentations of the current video frame; and', 'select one of the candidate segmentations as a final segmentation corresponding to the current video frame., 'one or more processors coupled to the memory, the one or more processors to2. The system of claim 1 , the one or more processors to:apply the classification convolutional neural network to the current video frame;retrieve, for each pixel of the current video frame, a plurality of feature values each from one of the layers of the classification convolutional neural network to generate a ...

IMAGE PROCESSING APPARATUS

An image processing apparatus () has a processing device () for executing, on a first image () captured by a first imaging device () imaging a first imaging area and a second image () captured by a second imaging device () imaging a second imaging area, an image recognition processing for recognizing a target in the first image and the second image, the processing device executes the image recognition processing on a non-overlapped image part () of the first image without executing it on a overlapped image part () of the first image when the processing device executes the image recognition processing on the first image, the non-overlapped image part includes a non-overlapped area at which the first imaging area does not overlap with the second imaging area, the overlapped image part includes a overlapped area at which the first imaging area overlaps with the second imaging area. 1. An image processing apparatus comprising a controller ,the controller being programmed to:receive a first image obtained by a first imager imaging a first imaging area and a second image obtained by a second imager imaging a second imaging area, a focal length of the second imager being longer than a focal length of the first imager, the second imaging area partially overlapping with the first imaging area; andexecute, on at least one portion of the first image and the second image, an image recognition processing for recognizing a target in at least one of the first image and the second image,the controller being programmed to execute the image recognition processing on a non-overlapped image part of the first image without executing the image recognition processing on a overlapped image part of the first image when the image recognition processing is executed on the first image, the non-overlapped image part including a non-overlapped area at which the first imaging area does not overlap with the second imaging area, the overlapped image part including a overlapped area at which the ...

Method and system for extracting lower limb vasculature

The present disclosure relates to systems and methods for extracting a vessel of a lower limb. The methods may include obtaining an original image including a plurality of image data, in some embodiments, each of the plurality of image data may correspond to a pixel (or a voxel), the plurality of image data may include a target data set, the target data set may represent a first structure; extracting a first reference data set from the plurality of image data, in some embodiments, the first reference data set may include the target data set and a second reference data set, the second reference data set may include data of a second structure; extracting the second reference data set from the plurality of image data; and obtaining the target data set based on the first reference data set and the second reference data set.

ORIENTATION ADJUSTMENT OF OBJECTS IN IMAGES

Aspects of the present disclosure relate to systems and methods for adjusting an orientation in an image. A device may include one or more processors coupled to a memory. The one or more processors may be configured to receive a first frame captured by a first camera from a first perspective. The first frame includes a first depiction of an object with a first orientation. The one or more processors also may be configured to receive a second frame captured by a second camera from a second perspective. The first frame and the second frame are captured concurrently, and the second frame includes a second depiction of the object with a second orientation. The one or more processors are further configured to determine a final orientation of the object based on the first orientation and the second orientation, and generate a final image depicting the object with the final orientation. 1. A device configured to adjust an orientation in an image , comprising:a memory; and receive a first frame captured by a first camera from a first perspective, wherein the first frame includes a first depiction of an object with a first orientation;', 'receive a second frame captured by a second camera from a second perspective, wherein the first frame and the second frame are captured concurrently and wherein the second frame includes a second depiction of the object with a second orientation;', 'determine a final orientation of the object based on the first orientation and the second orientation; and', 'generate a final image depicting the object with the final orientation., 'one or more processors implemented in circuitry, coupled to the memory, and configured to2. The device of claim 1 , wherein the one or more processors claim 1 , in determining the final orientation of the object claim 1 , are configured to:select the first orientation as the final orientation of the object.3. The device of claim 2 , wherein the one or more processors claim 2 , in generating the final image claim 2 ...

SPEED DETECTION SYSTEM OF PASSENGER CONVEYOR AND SPEED DETECTION METHOD THEREOF

The present invention relates to the field of passenger conveyor technologies, and provides a speed detection system of a passenger conveyor and a speed detection method thereof. In the speed detection system and the speed detection method of the present invention, an imaging sensor and/or a depth sensing sensor are/is used to sense at least one part of the passenger conveyor to acquire sequence frames, and the sequence frames are analyzed by a processing apparatus to obtain at least speed information about steps of the passenger conveyor. 1. A speed detection system of a passenger conveyor , comprising:an imaging sensor and/or a depth sensing sensor configured to sense at least one part of the passenger conveyor to acquire sequence frames; and an optical flow estimation module configured to calculate, based on an optical flow method, a shift of a corresponding feature point between any adjacent frames in the sequence frames in frame coordinates;', 'a calibration module configured to convert the shift of the feature point in the frame coordinates to a shift in three-dimensional space coordinates;', 'a time calculation module configured to determine a time quantity between any adjacent frames in the sequence frames; and', 'a speed calculation module configured to obtain by calculation, based on the shift of the feature point in the three-dimensional space coordinates and the corresponding time quantity, speed information of time points corresponding to any adjacent frames, and further combine the speed information to obtain speed information of the sequence frames., 'a processing apparatus configured to analyze the sequence frames to obtain at least speed information about steps of the passenger conveyor, which is configured to comprise2. The speed detection system of claim 1 , wherein the speed detection system is configured to detect at least speed information about steps of the passenger conveyor under a brake working condition or a startup working condition.3. ...

METHOD OF GENERATING THREE-DIMENSIONAL MODEL, DEVICE FOR GENERATING THREE-DIMENSIONAL MODEL, AND STORAGE MEDIUM

A method of generating a three-dimensional model includes: calculating camera parameters of n cameras based on m first images, the m first images being captured from m different viewpoints by the n cameras, n being an integer greater than one, m being an integer greater than n; and generating the three-dimensional model based on n second images and the camera parameters, the n second images being captured from n different viewpoints by the n cameras, respectively. 1. A method of generating a three-dimensional model , the method comprising:calculating camera parameters of n cameras based on m first images, the m first images being captured from m different viewpoints by the n cameras, n being an integer greater than one, m being an integer greater than n; andgenerating the three-dimensional model based on n second images and the camera parameters, the n second images being captured from n different viewpoints by the n cameras, respectively.2. The method according to claim 1 , whereinthe m first images are captured from the m different viewpoints by the n cameras and an additional camera, andan additional camera parameter of the additional camera is calculated based on the m first images.3. The method according to claim 1 , further comprising:generating a free viewpoint video based on (1) 1 third images respectively captured by 1 cameras included in the n cameras, where 1 is an integer greater than or equal to two and less than n, (2) the camera parameters calculated in the calculating, and (3) the three-dimensional model generated in the generating of the three-dimensional model.4. The method according to claim 2 , whereinin the calculating, (1) first camera parameters that are camera parameters of a plurality of cameras including the n cameras and the additional camera are calculated based on the m first images captured by the plurality of cameras, and (2) second camera parameters that are the camera parameters of the n cameras are calculated based on the first ...

System and Method for Mapping and Quantifying In-Vivo Blood Flow Stasis

Described here are systems and methods for producing an image that depicts blood flow stasis using magnetic resonance imaging (MRI), Doppler echocardiography, or other medical instruments for measuring flow velocities in a human body. A time series of three-dimensional (3D) image volumes is provided, where this time series of 3D image volumes contains flow velocity information at voxel locations in a 3D volume in a subject. One or more regions-of-interest are then segmented from the 3D image volumes. For each voxel in the regions-of-interest, velocity magnitudes are calculated. Using the velocity magnitudes, a flow stasis volume is produced by computing a relative stasis value for each voxel location in the corresponding region-of-interest. This flow stasis volume can be provided as a 3D flow stasis image, or a flow stasis map can be produced by projecting the flow stasis volume onto a two-dimensional (2D) plane. 1. A method for producing an image that depicts blood flow stasis using a medical imaging system , the steps of the method comprising:(a) providing a time series of images acquired with the medical imaging system, the time series of images containing flow velocity information at voxel locations in a three-dimensional volume;(b) segmenting at least one region-of-interest from each image in the time series of images;(c) calculating a velocity magnitude for each voxel in each region-of-interest;(d) producing a flow stasis volume by computing a relative stasis value for each voxel location in each region-of-interest by determining, at a given voxel location, a number of time frames in the time series of images having a velocity magnitude below a user-selected threshold value and normalizing the determined number of time frames at the given voxel location by a total number of time frames in the times series of images; and(e) generating an image that depicts blood flow stasis from the produced flow stasis volume.2. The method as recited in claim 1 , wherein step ...

VEHICLE ACCESSIBILITY DETERMINATION DEVICE

An image convertor converts an original image captured by a front camera. The original image includes a road surface around a vehicle. A three-dimensional object detector detects from a virtual image a three-dimensional object having a height from the road surface. A vehicle accessibility determiner determines whether the vehicle can access to the inside of the detected three-dimensional object or to a clearance among other three-dimensional objects. 1. A vehicle accessibility determination device comprising:an imager that is attached to a vehicle and captures a range including a road surface around the vehicle;an image convertor that converts an original image captured by the imager into a virtual image viewed from a predetermined viewpoint;a three-dimensional object detector that detects from the virtual image a three-dimensional object having a height from the road surface; anda vehicle accessibility determiner that determines whether the vehicle is capable of accessing to an inside of the three-dimensional object or to a clearance among other three-dimensional objects.2. The vehicle accessibility determination device according to claim 1 ,wherein the vehicle accessibility determiner comprises:a three-dimensional object area extracting portion that extracts an area corresponding to the three-dimensional object from the original image;a road surface projecting position calculating portion that calculates a presence or an absence of a floating area that does not contact the road surface and a height of the floating area from the road surface relative to the three-dimensional object area extracted by the three-dimensional object area extracting portion, and calculates a road surface projecting position in which the floating area is projected to the road surface from directly above, the floating area constituting the three-dimensional object area; anda vehicle accessible space identifying portion that identifies whether there is a space inside the three-dimensional ...

METHODS AND APPARATUS FOR CLASSIFYING AN ARTIFACT IN A SPECIMEN

A model-based method of inspecting a specimen for presence of one or more artifacts (e.g., a clot, bubble, and/or foam). The method includes capturing multiple images of the specimen at multiple different exposures and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, computing statistics of the optimally-exposed pixels to generate statistical data, identifying a serum or plasma portion of the specimen, and classifying, based on the statistical data, whether an artifact is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects. 1. A method of determining an artifact in a specimen contained within a specimen container , comprising:providing a specimen that is separated and contained in a specimen container;capturing images of the specimen at multiple different exposures and at multiple different wavelengths;selecting optimally-exposed pixels from the captured images at the different exposures at each wavelength to generate optimally-exposed image data for each wavelength;computing statistics on the optimally-exposed pixels at the different wavelengths to generate statistical data;identifying a serum or plasma portion of the specimen based on the statistical data; and present within one or more regions of the serum or plasma portion, or', 'absent within the serum or plasma portion., 'classifying, based on the statistical data, whether an artifact is2. The method of claim 1 , wherein the artifact is selected from a group including at least one of a clot claim 1 , a bubble claim 1 , or foam.3. The method of claim 1 , wherein the specimen is centrifuged and includes a separated blood portion and a serum or plasma portion.4. The method of claim 1 , wherein the capturing images of the specimen involves capturing multiple images taken ...

3d radiomic platform for imaging biomarker development

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

Reconstruction of images from an in vivo multi-camera capsule

Method and apparatus of reconstruction of images from an in vivo multi-camera capsule are disclosed. In one embodiment of the present invention, the capsule comprises two cameras with overlapped fields of view (FOVs). Intra-image based pose estimation is applied to the sub-images associated with the overlapped area to improve the pose estimation for the capsule device. In another embodiment, two images corresponding to the two FOVs are fused by using disparity-adjusted, linear weighted sum of the overlapped sub-images. In yet another embodiment, the images from the multi-camera capsule are stitched for time-space representation.

Systems and methods for automatic vertebrae segmentation and identification in medical images

The present disclosure relates to a method, system and non-transitory computer readable medium. In some embodiments, the method includes: acquiring a plurality of image slices related to a vertebral column, the vertebral column including a plurality of vertebrae; obtaining a classifier for verterbrae identification; identifying, by a processor, one or more vertebral foramina in the plurality of image slices using the classifier; and determining, by the processor, the plurality of vertebrae based on the one or more vertebral foramina.

Method and system for layered real-time graphics drawing and rendering

A method and system for drawing and rendering a graphic in real-time in a layered manner are provided. Adjacent application frames spaced with a time period are extracted, a scene change rate is calculated, and whether the scene change rate exceeds a preset change rate threshold is determined. If the scene change rate does not exceed the preset change rate threshold, graphic elements are grouped to a same graphic layer. If the scene change rate exceeds the preset change rate threshold, M application frames chronologically located between the adjacent application frames are extracted. Each application frame is divided into N regions, and a scene change rate of adjacent application frames is calculated for each region. An average scene change rate for each region is calculated, a layering operation is performed based on the average scene change rate, and graphic layers are drew and rendered separately.

Real Time Perspective Correction on Faces

Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh. 1. A computer-implemented method , comprising:determining a first image area in an image;determining a warping mesh for the image;determining a first portion of the warping mesh, wherein the first portion of the warping mesh is associated with the first image area;determining a cost function for the warping mesh by determining first costs associated with the first portion of the warping mesh, wherein the first costs comprise costs associated with one or more face-related transformations to correct one or more geometric distortions in the first image area;determining an optimized mesh based on an optimization of the cost function for the warping mesh; andmodifying the first image area in the image based on the optimized mesh.2. The computer-implemented method of claim 1 , further comprising:determining a second image area in the image, the second image area differing from the first image area; anddetermining a second portion of the warping mesh, wherein the second portion of the warping mesh is associated with the second image area, wherein determining the cost function for the warping mesh further comprises determining additional first costs associated with the second portion of the warping mesh, and wherein the ...

Business discovery from imagery

Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

BEAUTY COUNSELING INFORMATION PROVIDING DEVICE AND BEAUTY COUNSELING INFORMATION PROVIDING METHOD

Disclosed is a beauty counseling information providing method and apparatus, which may generate and provide beauty counseling information by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet-of-Things. A beauty counseling information providing method according to an embodiment of the present disclosure may include generating a first image set that has classified a plurality of images previously stored based on capturing information every predetermined period, generating a second image set that has classified a plurality of images included in the first image set based on the purpose of providing counseling information, calculating a body feature through comparative analysis of the plurality of images included in the second image set, and providing the beauty counseling information when the amount of change between the calculated body feature and previously stored existing body feature exceeds a predetermined value. 1. A method for providing beauty counseling information , the method comprising:generating a first image set including a plurality of first images previously stored based on capturing image information on a periodic basis;generating a second image set including a plurality of second images included in the first image set, the second image set being classified based on one or more conditions for providing counseling information;calculating a current condition of a body feature of a user based on a comparative analysis of the plurality of second images included in the second image set; andin response to an amount of change between the current condition of the body feature and a previous condition of the body feature exceeding a predetermined value, generating beauty counseling information.2. The method of claim 1 , wherein the generating the first image set includes:classifying the plurality of first images based on a self-capturing mode in which the user directly captures ...

IMAGE PROCESSING APPARATUS AND IMAGE PROCESSING METHOD

An image processing method according to an embodiment includes an image acquisition unit, a calculation unit, a region acquisition unit and an estimation unit. The image acquisition unit acquires a target image. The calculation unit calculates a density distribution of targets included in the target image. The estimation unit estimates the density distribution in a first region in the target image based on the density distribution in a surrounding region of the first region in the target image. 1. An image processing apparatus comprising:a memory; and an image acquisition unit that acquires a target image;', 'a calculation unit that calculates a density distribution of targets included in the target image;', 'and', 'an estimation unit that estimates the density distribution in a first region in the target image based on the density distribution in a surrounding region of the first region in the target image., 'processing circuitry configured to operate as2. The image processing apparatus according to claim 1 , wherein the estimation unit estimates the density distribution in the first region by performing polynomial interpolation of the density distribution in the surrounding region in the target image.3. The image processing apparatus according to claim 1 , wherein the estimation unit estimates the density distribution in the first region using an average value of densities represented by the density distribution in the surrounding region in the target image.4. The image processing apparatus according to claim 1 , wherein the estimation unit estimates a density distribution in the first region in the target image from the density distribution in the surrounding region in the target image using a function representing a regression plane or a regression curve that approximates a density distribution in the target image based on densities in areas included in the surrounding region in the target image.5. The image processing apparatus according to claim 1 , wherein ...

SUBSTRATE INSPECTION METHOD AND SYSTEM

A substrate inspection system and substrate inspection method for setting an inspection region having a three-dimensional shape and/or a two-dimensional arbitrary shape as a region of interest on an image of a substrate. The substrate inspection method includes: generating and displaying a 2D image of a substrate based on image data acquired from the substrate having an inspection object; receiving first input information including arbitrary point data or line data for setting a region of interest at a plurality of particular positions of the 2D image from a user; and displaying the region of interest corresponding to the point data or the line data as a 2D region of interest having an arbitrary shape in accordance with the first input information. 1. A method of inspecting a substrate , the method comprising:generating and displaying a 2D image of a substrate based on image data acquired from the substrate having an inspection object;receiving first input information including arbitrary point data or line data for setting a region of interest at a plurality of particular positions of the 2D image from a user; anddisplaying the region of interest corresponding to the point data or the line data as a 2D region of interest having an arbitrary shape in accordance with the first input information,wherein the 2D region of interest is resettable by combination or division.2. (canceled)3. (canceled)4. The method of claim 1 , wherein the 2D region of interest is at least two regions of interest movable by dragging claim 1 , andwherein the at least two 2D regions of interest are capable of being reset as one 2D region of interest.5. The method of claim 4 , wherein the at least two 2D regions of interest are capable of being reset as one 2D region of interest by combining the at least two 2D regions of interest with each other.6. The method of claim 4 , wherein the displaying of the 2D region of interest comprises:selecting a first 2D region of interest among the at least two ...

METHOD FOR DETERMINING THE OFFSET BETWEEN THE CENTRAL AND OPTICAL AXES OF AN ENDOSCOPE

Disclosed is a method for determining the offset or misalignment between the central or rotational axis and the optical axis of a rigid endoscope or a similar imaging device including a rigid body having an outer casing cylindrically-shaped in the direction of the optical axis, or including at least one segment having a rigid end with such a casing. The method includes taking a plurality of images with a field of view limited by a contour, the positioning of which relative to the central axis is, for each image, physically defined and specific, a relative angular rotation between the contour and the endoscope taking place between two successive images, and of determining a point or a pixel in the successively acquired images whose position remains unchanged, the point corresponding to the projection in the image plane of the central or rotational axis of the rigid body of the endoscope. 1. Procedure for determining the offset or misalignment between the central or rotating axis and the optical axis of a rigid endoscope or similar camera device , consisting of a rigid body with a cylindrical external casing , profiled in the direction of the optical axis , or consisting of at least one rigid segment end with such a casing ,{'b': 3', '1', '4', '4', '1', '3', '2', '1, 'the procedure comprising taking a series of shots, using a camera or similar sensory device () that is part of the endoscope or similar device (), with a field of vision restricted by a contour () that is polygonal, circular, or elliptical in shape, whose positioning in relation to the central or rotating axis (Δ) for each shot is physically defined and specific, with a relative angular rotation between the contour () and the endoscope or similar device () intervening between two successive shots, and determining a point or a pixel (PI, CΔ) in the images acquired successively whose position remains unchanged between the various shots, this point or pixel (PI, CΔ) corresponding to the projection in the ...

SPATIOTEMPORAL RECYCLING NETWORK

Systems, methods, and non-transitory media are provided for providing spatiotemporal recycling networks (e.g., for video segmentation). For example, a method can include obtaining video data including a current frame and one or more reference frames. The method can include determining, based on a comparison of the current frame and the one or more reference frames, a difference between the current frame and the one or more reference frames. Based on the difference being below a threshold, the method can include performing semantic segmentation of the current frame using a first neural network. The semantic segmentation can be performed based on higher-spatial resolution features extracted from the current frame by the first neural network and lower-resolution features extracted from the one or more reference frames by a second neural network. The first neural network has a smaller structure and/or a lower processing cost than the second neural network. 1. A method of processing one or more frames , comprising:obtaining video data including a current frame and one or more reference frames;determining, based on a comparison of the current frame and the one or more reference frames, a difference between the current frame and the one or more reference frames; andbased on the difference being below a threshold, performing semantic segmentation of the current frame using a first neural network based on higher-spatial resolution features extracted from the current frame and lower-spatial resolution features extracted from the one or more reference frames by a second neural network, the first neural network having at least one of a smaller structure and a lower processing cost than the second neural network.2. The method of claim 1 , wherein the second neural network comprises a segmentation network and the first neural network comprises a subnetwork structure of the segmentation network.3. The method of claim 1 , wherein the difference comprises a difference between pixel ...

Vision system for vehicle

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

System and method for providing stroke lesion segmentation using conditional generative adversarial networks

A system and method for performing image processing. The method includes receiving an image of a first modality and a real image of a second modality, the image of the first modality and the image of the second modality capturing respective images of a same subject, applying a first trained model to the image of the first modality to generate an artificial image mimicking the image of the second modality, applying a second trained model to the artificial image mimicking the image of the second modality and data of the image of the first modality, and outputting at least one conclusion regarding the generated artificial image.

Image processing device, method, and program

Labels can be accurately identified even for an image with a resolution not used in training data. Based on an input image, a resolution of the input image, and a resolution of a training image used for training a trained model of assigning labels to pixels of an image, a plurality of low-resolution images are generated from the input image by using a plurality of shift amounts for a pixel correspondence between the input image and the respective low-resolution images with a resolution corresponding to the training image, the low-resolution images are input to the trained model, a plurality of low-resolution label images is output in which pixels of the respective low-resolution images are assigned labels, and a label image is output in which labels for pixels of the input image are obtained, based on the shift amounts used for generating the low-resolution images and the low-resolution label images.

Real Time Perspective Correction on Faces

Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh. 1. A computer-implemented method , comprising:determining a warping mesh for an image area in an image;determining, for the warping mesh, one or more first transformations to correct one or more geometric distortions in the image area;determining, for the warping mesh, one or more second transformations to preserve one or more boundaries of objects outside the image area; andmodifying the image area in the image based on the one or more first transformations and the one or more second transformations.2. The computer-implemented method of claim 1 , wherein the one or more first transformations comprises a conformal projection of the image area.3. The computer-implemented method of claim 1 , wherein the one or more second transformations comprises a perspective projection of a portion of the image outside the image area.4. The computer-implemented method of claim 1 , wherein the determining of the one or more second transformations to preserve the one or more boundaries of objects outside the image comprises determining the one or more second transformations to preserve straightness of edges represented outside the image area.5. The computer-implemented method of claim 1 , further comprising:determining the image area in ...

APPARATUS AND METHOD FOR EFFICIENT POINT CLOUD FEATURE EXTRACTION AND SEGMENTATION FRAMEWORK

A computer implemented scheme for a light detection and ranging (LIDAR) system where point cloud feature extraction and segmentation by efficiently is achieved by: (1) data structuring; (2) edge detection; and (3) region growing. 1. A machine-readable storage media having instructions stored thereon , that when executed , cause one or more processors to perform an operation for a LIDAR system where point cloud feature extraction and segmentation is performed , the operation comprising:receiving scan pattern information and point cloud data from the LIDAR system, wherein the scan pattern information and point cloud data are associated with a physical object;generating a scan pattern grid from the scan pattern information and point cloud data;detecting one or more silhouette edges using the scan pattern grid and the point cloud data;detecting one or more intersection edges using the scan pattern grid and the point cloud data; andgrowing a region based on the detected silhouette edge and intersection edge.2. The machine-readable storage media of claim 1 , wherein the scan pattern grid is a 2-D grid structure having cells organized in rows and columns claim 1 , wherein an individual row corresponds to a vertical angle of a scan from a scanner of the LIDAR system claim 1 , and wherein an individual column corresponds to a horizontal angle from the scanner of the LIDAR system.3. The machine-readable storage media of claim 1 , wherein the LIDAR system is a mobile LIDAR system claim 1 , wherein the scan pattern grid is a 2-D grid structure having cells organized in rows and columns claim 1 , wherein an individual row corresponds to a time of a scan from a scanner of the LIDAR system claim 1 , and wherein an individual column corresponds to a scan angle from the scanner of the LIDAR system.4. The machine-readable storage media of claim 2 , wherein an individual cell of the scan pattern grid represents a laser pulse from the scanner claim 2 , wherein the individual cell ...

METHOD FOR DETECTING VOIDS AND AN INSPECTION SYSTEM

A method for detecting voids in a metal line of a semiconductor device die includes: scanning an electron beam upon a selected location on the die containing the metal line; determine gray levels in an image produced by collected electrons of the electron beam backscattered from the selected location on the die; and identifying one or more voids in the metal line based on differences between the gray levels in the image. 1. A method for detecting voids in a semiconductor device substrate during fabrication , the method comprising:selecting or receiving a selection of locations within a semiconductor device substrate for void detection;with an electron beam imaging system, collecting electrons emanating from the semiconductor device substrate in response to impinging a high energy primary particle beam onto areas of the semiconductor device substrate corresponding to the selection of locations;generating a corresponding void image of the areas of the semiconductor device substrate;segmenting the void image into portions corresponding to metal lines on the semiconductor device substrate and portions corresponding to areas outside the metal lines on the semiconductor device substrate; andanalyzing the portions of the void image corresponding to the metal lines to detect the voids by identifying differences in gray level values.2. The method according to claim 1 , further comprising:providing a reference image; andcomparing the reference image with the void image to detect the voids in the metal lines, wherein the differences in gray level values are determined by comparing the void image with the reference image.3. The method according to claim 2 , wherein the reference image is a portion of the void image claim 2 , and comparing the reference image with the void image comprises comparing different portions of the void image corresponding to different areas of the semiconductor device substrate.4. The method according to claim 1 , wherein the voids are metal voids ...

Method and system of recurrent semantic segmentation for image processing

A system, article, and method of recurrent semantic segmentation for image processing by factoring historical semantic segmentation.

MULTI-CAMERA PROCESSOR WITH FEATURE MATCHING

An embodiment of a semiconductor package apparatus may include technology to capture two or more concurrent images of a scene with two or more cameras, detect a feature in a first image from a first camera of the two or more cameras, match the feature in a second image from a second camera of the two or more cameras, and perform a photometric calibration between the first camera and the second camera based on a portion of the first image corresponding to the detected feature and a portion of the second image corresponding to the matched feature. Other embodiments are disclosed and claimed. 1. An electronic processing system , comprising:a processor;two or more cameras communicatively coupled to the processor to capture two or more concurrent images of a scene; and detect a feature in a first image from a first camera of the two or more cameras,', 'match the feature in a second image from a second camera of the two or more cameras, and', 'perform a photometric calibration between the first camera and the second camera based on a portion of the first image corresponding to the detected feature and a portion of the second image corresponding to the matched feature., 'logic communicatively coupled to the processor to2. The system of claim 1 , wherein the logic is further to:define a first region of interest for the first camera based on the detected feature in the first image;define a second region of interest for the second camera based on the matched feature in the second image; andperform the photometric calibration between the first camera and the second camera based on the first region of interest and the second region of interest.3. The system of claim 2 , wherein the logic is further to:define the first and second regions of interest based on respective positions and sizes of the feature in the first and second images.4. The system of claim 1 , wherein the logic is further to:define a first region of interest for the first camera;detect the feature in the first ...

Method and Apparatus for Enhancing Stereo Vision

A method and apparatus for segmenting an image are provided. The method may include the steps of clustering pixels from one of a plurality of images into one or more segments, determining one or more unstable segments changing by more than a predetermined threshold from a prior of the plurality of images, determining one or more segments transitioning from an unstable to a stable segment, determining depth for one or more of the one or more segments that have changed by more than the predetermined threshold, determining depth for one or more of the one or more transitioning segments, and combining the determined depth for the one or more unstable segments and the one or more transitioning segments with a predetermined depth of all segments changing less than the predetermined threshold from the prior of the plurality of images. 1. A method for segmenting an image , comprising the steps of:defining one or more segments from one of a plurality of images;determining one or more unstable segments of the one or more segments changing by more than a predetermined threshold from a prior of the plurality of images;determining one or more transitioning segments transitioning from an unstable to a stable segment;determining depth only for one or more of the one or more unstable segments that have changed by more than the predetermined threshold and for one or more of the one or more transitioning segments; andcombining the determined depth for the one or more unstable segments and the one or more transitioning segments with a preexisting depth map.2. The method of wherein the transitioning segments comprise one or more mesostable segments.3. The method of claim 1 , wherein the segments are defined from a stereo pair of images.4. The method of claim 1 , wherein the determined depth for the one or more unstable and transitioning segments replaces a prior corresponding depth value in the preexisting depth map.5. The method of claim 1 , further comprising the step of generating a ...

Methods and systems for producing an implant

A computer implemented method for determining the 3-dimensional shape of an implant to be implanted into a subject includes obtaining a computer readable image including a defective portion and a non-defective portion of tissue in the subject, superimposing on the image a shape to span the defective portion, and determining the 3-dimensional shape of the implant based on the shape that spans the defective portion.

MEDICAL IMAGE PROCESSING DEVICE, SYSTEM, METHOD, AND PROGRAM

[Object] To improve accuracy of determination of a disparity for medical image processing. 1. A medical image processing device comprising:a region determination unit configured to determine a non-living body region in a visual field by using a region-determination image showing the visual field that is same as a visual field that a disparity-determination image shows; anda disparity determination unit configured to determine at least a disparity to be applied to the non-living body region by using a result of the determination of the non-living body region performed by the region determination unit and the disparity-determination image.2. The medical image processing device according to claim 1 , whereinthe disparity determination unit determines the disparity by collating a first disparity-determination image with a second disparity-determination image by using a collation block that is set on a basis of the result of the determination of the non-living body region.3. The medical image processing device according to claim 2 , whereinthe disparity determination unit sets a size of the collation block depending on a size or shape of the non-living body region.4. The medical image processing device according to claim 2 , whereinthe disparity determination unit sets a weight of each pixel in the collation block depending on a shape of the non-living body region, the weight being used for the collation.5. The medical image processing device according to claim 4 , whereinthe disparity determination unit sets the weight of a pixel corresponding to the non-living body region in the collation block to a first value and sets the weight of a pixel corresponding to a living body region in the collation block to a second value, andthe second value means that a pixel value at a corresponding pixel position is not added to an index for the collation or is added to the index as an extremely small value.6. The medical image processing device according to claim 4 , whereinthe ...

SYSTEM AND METHOD FOR STREAMING VISIBLE PORTIONS OF VOLUMETRIC VIDEO

Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: predicting a viewpoint within a volumetric video based on movement input by a viewer, resulting in a predicted viewpoint, wherein a point cloud of the volumetric video is segmented into cells, and wherein at least some of the cells are visible to the viewer at a future time of playback based on the predicted viewpoint; retrieving a cell occupancy bitmap for the point cloud for the future time of playback; determining visible cells based on the cell occupancy bitmap and the predicted viewpoint; receiving the visible cells for the future time of playback; and rendering and displaying the point cloud within the visible cells during playback at the future time. Other embodiments are disclosed. 1. A device , comprising:a processing system including a processor; and predicting a viewpoint within a volumetric video based on a movement input provided by a viewer, resulting in a predicted viewpoint, wherein a point cloud of the volumetric video is segmented into three-dimensional cells, and wherein at least some of the cells are visible to the viewer at a future time of playback based on the predicted viewpoint;', 'retrieving a cell occupancy bitmap for the point cloud for the future time of playback;', 'determining visible cells based on the cell occupancy bitmap and the predicted viewpoint, wherein the visible cells are not obscured by points in other cells;', 'requesting the visible cells for the future time of playback; and', 'rendering points of the point cloud that are within the visible cells during playback at the future time of playback., 'a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising2 ...

IMAGE PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

An image processing apparatus which extracts a region of a foreground object in a captured image obtained by an imaging unit, and controls a display unit to display an output image formed by superimposing, on a predetermined image, a first image formed by anonymizing the extracted region, and a second image formed by anonymizing a region designated by a user on the captured image, wherein an appearance of the second image is different from an appearance of the first image, a region of a foreground object is extracted from a region other than at least the designated region in the captured image, and even when the designated region contains the foreground object, the first image in the designated region is not displayed on the output image. 1. An image processing apparatus comprising:an extraction unit configured to extract a region of a foreground object in a captured image obtained by an imaging unit; anda display control unit configured to cause a display unit to display an output image formed by superimposing, on a predetermined image, a first image formed by anonymizing the region extracted by the extraction unit, and a second image formed by anonymizing a region designated by a user on the captured image obtained by the imaging unit,wherein an appearance of the second image is different from an appearance of the first image,the extraction unit extracts a region of a foreground object from a region other than at least the designated region in the captured image obtained by the imaging unit, andeven when the designated region contains the foreground object, the first image formed by anonymizing the region of the foreground object in the designated region is not displayed on the output image displayed on the display unit by the display control unit.2. The apparatus according to claim 1 , wherein the extraction unit extracts the region of the foreground object based on a difference between a background image and the captured image.3. The apparatus according to claim ...

MEASUREMENT OF BODY TEMPERATURE OF A SUBJECT

There is provided a computer implemented method of measuring a temperature of a subject, comprising: receiving a sequence of a plurality of thermal images of a subject captured by a thermal sensor, analyzing the sequence of the plurality of thermal images to identify at least one target thermal image depicting an upper region of a tongue of the subject, analyzing the at least one target thermal image to identify an estimated temperature of the upper region of the tongue, and providing the estimated temperature of the upper region of the tongue.

Rapid anomaly detection (rand)

A rapid anomaly detection approach with corresponding method and system to detect anomalies in scene pixels making up a hyperspectral scene, efficiently, is presented. The approach includes tailoring an approximation of an anomaly score for each scene pixel, individually, based on an “intermediate anomaly score.” The intermediate score is computed using a portion of the terms used to compute the anomaly score. Scene pixels with low intermediate anomaly scores are removed from further processing. The remaining scene pixels are further processed, including computing anomaly scores to detect anomalies in these pixels. Advantageously, examples of the RAND approach process a few terms of all scene pixels, eliminate most scene pixels, and calculate more terms on high anomaly scoring scene pixels as needed.

SYSTEM AND METHODS FOR IMAGE SEGMENTATION USING CONVOLUTIONAL NEURAL NETWORK

The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack. 1. A computer-implemented method for training a machine learning model to segment a three-dimensional medical image , the method comprising:receiving, by a processor, the three-dimensional medical image to be segmented;selecting a first stack of two-dimensional images corresponding to a first plane of the three-dimensional medical image;extracting one or more features of the first stack of two-dimensional images;generating a training label map for an image in the first stack of two-dimensional images based on the extracted one or more features; andtraining the machine learning model to generate a new label map for a subsequently received three-dimensional medical image based on a comparison of the generated training label map with a ground truth label map corresponding to the first stack of two-dimensional images.2. The method of claim 1 , further comprising:selecting a second stack of two-dimensional images from a second plane of the three-dimensional medical image; andgenerating a training label map for an image in the second stack of two-dimensional images.3. The method of claim 2 , ...

GROSS TUMOR VOLUME SEGMENTATION METHOD AND COMPUTER DEVICE

In a GTV segmentation method, a PET-CT image pair and an RTCT image of a human body are obtained. A PET image in the PET-CT image pair is aligned to the RTCT image to obtain an aligned PET image. A first PSNN performs a first GTV segmentation on the RTCT image to obtain a first segmentation image. The RTCT image and the aligned PET image are concatenated into a first concatenated image. A second PSNN performs a second GTV segmentation on the first concatenated image to obtain a second segmentation image. The RTCT image, the first segmentation image, and the second segmentation image are concatenated into a second concatenated image. A third PSNN performs a third GTV segmentation on the second concatenated image to obtain an object segmentation image. 1. A gross tumor volume (GTV) segmentation method being executed by a computer device , the method comprising:obtaining a positron emission tomography-computer tomography (PET-CT) image pair and a radiotherapy computer tomography (RTCT) image of a predetermined part of a human body;aligning a PET image in the PET-CT image pair to the RTCT image to obtain an aligned PET image;using a first progressive semantically nested network (PSNN) to perform a first GTV segmentation on the RTCT image to obtain a first segmentation image;concatenating the RTCT image and the aligned PET image into a first concatenated image;using a second PSNN to perform a second GTV segmentation on the first concatenated image to obtain a second segmentation image;concatenating the RTCT image, the first segmentation image, and the second segmentation image into a second concatenated image; andusing a third PSNN to perform a third GTV segmentation on the second concatenated image to obtain an object segmentation image;wherein the first PSNN, the second PSNN, and the third PSNN reverse a direction of deeply-supervised pathways are based on a progressive holistically nested network (P-HNN) to propagate high-level features to lower-level features.2. The ...

REGION EXTRACTION APPARATUS AND REGION EXTRACTION METHOD

A region extraction apparatus provided with a candidate extraction unit, a categorization unit and a region extraction unit. The candidate extraction unit extracts a difference region where a first ground surface image photographed from above at a first time point and a second ground surface image photographed at a second time point are different. The categorization unit estimates a first category of a first object photographed in correspondence with a position in the first ground surface image, calculates a first categorization image indicating a relationship between the position of the first object and the first category, estimates a second category of a second object photographed in correspondence with a position in the second ground surface image and calculates a second categorization image indicating a relationship between the position of the second object and the second category. The region extraction unit extracts an extraction region from the difference region. 1. A region extraction apparatus comprising:a candidate extraction unit configured to extract a difference region where a first ground surface image of a ground photographed from above at a first time point and a second ground surface image of the ground photographed at a second time point different from the first time point are different;a categorization unit configured to estimate a first category of a first object that is photographed in correspondence with a position in the first ground surface image, calculate a first categorization image that indicates a relationship between the position of the first object and the first category, estimate a second category of a second object that is photographed in correspondence with a position in the second ground surface image, and calculate a second categorization image that indicates a relationship between the position of the second object and the second category; anda region extraction unit configured to extract an extraction region, in which the first ...

DEFECT INSPECTION METHOD AND DEFECT INSPECTION APPARATUS

In a defect inspection method, first and second inspection conditions having a first sensitivity of detection signal and having a second sensitivity of a detection signal for a defect of interest (DOI), respectively, are determined. The first and second sensitivities are different. First and second images of the same detection region on a substrate surface under the first and second inspection conditions respectively, are obtained. The first and second images are matched to detect a defect in the detection region. 1. A defect inspection method , comprising:determining first and second inspection conditions having a first sensitivity of detection signal and having a second sensitivity of a detection signal for a defect of interest (DOI), respectively, the first and second sensitivities being different;obtaining first and second images of the same detection region on a substrate surface under the first and second inspection conditions respectively; andmatching the first and second images to detect a defect in the detection region.2. The method of claim 1 , wherein a detection signal value for the DOI under the first inspection condition is greater than a detection signal value for the DOI under the second inspection condition.3. The method of claim 1 , wherein the detection signal comprises a signal-to-noise ratio (SNR) signal.4. The method of claim 1 , wherein a difference value between detection signal values for the DOI under the first and second inspection conditions is greater than a difference value between detection signal values for a defect of non-interest under the first and second inspection conditions.5. The method of claim 1 , wherein obtaining the first and second images comprises scanning the substrate surface with light energy using an optical microscope.6. The method of claim 1 , wherein obtaining the first and second images comprises scanning the substrate surface with an electron beam using an electron microscope.7. The method of claim 1 , wherein ...

OBJECT ATTRIBUTION ANALYZING METHOD AND RELATED OBJECT ATTRIBUTION ANALYZING DEVICE

An object attribution analyzing method applied to an object attribution analyzing device and includes dividing a plurality of continuous frames into a current frame and several previous frames, utilizing face detection to track and compute a first attribution predicted value of an object within the current frame, utilizing the face detection to acquire a feature parameter of the object within the current frame for setting a first weighting, acquiring a second attribution predicted value of the object within the several previous frames, setting a second weighting in accordance with the first weighting, and generating a first induction attribution predicted value of the object within the plurality of continuous frames via the first attribution predicted value weighted by the first weighting and the second attribution predicted value weighted by the second weighting. 1. An object attribution analyzing method , comprising:dividing a plurality of continuous frames into a current frame and several previous frames;utilizing face detection to track and compute a first attribution predicted value of an object within the current frame;utilizing the face detection to acquire a feature parameter of the object within the current frame for setting a first weighting;acquiring a second attribution predicted value of the object within the several previous frames;setting a second weighting in accordance with the first weighting; andgenerating a first inducted attribution predicted value of the object within the plurality of continuous frames via the first attribution predicted value weighted by the first weighting and the second attribution predicted value weighted by the second weighting.2. The object attribution analyzing method of claim 1 , further comprising:acquiring a latest frame after the plurality of continuous frames;utilizing the face detection to track and compute a third attribution predicted value of the object within the latest frame;utilizing the face detection to ...

SEGMENTING AN ANGIOGRAPHY USING AN EXISTING THREE-DIMENSIONAL RECONSTRUCTION

A method for segmenting a two-dimensional angiographic recording of a vessel of a body using a computing apparatus includes providing a three-dimensional reconstruction of the vessel of the body to the computing apparatus. The two-dimensional angiographic recording of the vessel of the body is provided on the computing apparatus. The three-dimensional reconstruction of the vessel of the body is registered with the two-dimensional recording of the vessel of the body. Spatial information of the three-dimensional reconstruction is projected onto the two-dimensional recording, and the two-dimensional recording is segmented using the spatial information projected onto the two-dimensional recording. 1. A method for segmenting a two-dimensional angiographic recording of a vessel of a body by a computing apparatus , the method comprising:providing a three-dimensional reconstruction of the vessel of the body on the computing apparatus;providing the two-dimensional angiographic recording of the vessel of the body on the computing apparatus;registering the three-dimensional reconstruction of the vessel of the body with the two-dimensional angiographic recording of the vessel of the body;projecting spatial information of the three-dimensional reconstruction onto the two-dimensional angiographic recording; andsegmenting the two-dimensional angiographic recording using the spatial information projected onto the two-dimensional angiographic recording.2. The method of claim 1 , wherein the spatial information comprises at least one centerline of the vessel of the body that is projected onto the two-dimensional angiographic recording claim 1 , and based on the at least one projected centerline claim 1 , a centerline for the vessel of the body is determined on the two-dimensional angiographic recording and is used in the segmentation.3. The method of claim 2 , wherein the at least one projected centerline is determined as a centerline for the vessel of the body on the recording and ...

APPARATUS, METHODS AND ARTICLES FOR FOUR DIMENSIONAL (4D) FLOW MAGNETIC RESONANCE IMAGING

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: perform error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets. 1. (canceled)2. A method of operation for use with magnetic resonance imaging (MRI) based medical imaging systems , the method comprising:receiving a set of MRI data by at least one processor-based device, the set of MRI data comprising respective anatomical structure and blood flow information for each of a plurality of voxels; andapplying a first filter to isolate blood flow based on directional coherence to at least a portion of the received set of MRI data by the at least one processor-based device.3. The method of wherein applying a first filter to isolate blood flow based on directional coherence to at least a portion of the received set of MRI data includes:for each of a number of voxels, calculating a directional coherence for the respective voxel.4. The method of wherein calculating a directional coherence for a respective voxel includes:summing a set of weighted directional coherence scores between the respective voxel and a plurality of neighboring voxels which are neighbors of the respective voxel; anddividing a result of the summation by a summation of all weights applied.5. The method of claim 4 , further comprising:determining the weighted ...

IMAGE IDENTIFICATION APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM

An image identification apparatus includes an extraction unit, an excluding unit, and an identification unit. The extraction unit extracts lines from an image. The exclusion unit excludes from objects to be identified a boundary delimiting an entire area of the image among the extracted lines. The identification unit identifies as an object multiple lines that are among the extracted lines and that are not excluded by the exclusion unit if the multiple lines are connected to each other. 1. An image identification apparatus comprising:an extraction unit that extracts lines from an image;an exclusion unit that excludes from objects to be identified a boundary delimiting an entire area of the image among the extracted lines; andan identification unit that identifies as an object a plurality of lines that are among the extracted lines and that are not excluded by the exclusion unit if the plurality of lines are connected to each other.2. The image identification apparatus according to claim 1 , wherein the exclusion unit excludes as a region that corresponds to the boundary delimiting the entire area of the image a region that is largest among regions that are included in the image and in which pixels of the extracted lines are aligned continuously.3. The image identification apparatus according to claim 1 , wherein the exclusion unit excludes a portion of the boundary delimiting the entire area of the image claim 1 , the portion being located in a region that is not included in a region that overlaps a table region extracted from the image.4. The image identification apparatus according to claim 2 , wherein the exclusion unit excludes a portion of the boundary delimiting the entire area of the image claim 2 , the portion being located in a region that is not included in a region that overlaps a table region extracted from the image.5. The image identification apparatus according to claim 1 , wherein the exclusion unit excludes as the boundary delimiting the entire area ...

METHOD OF DETECTING A SNOW COVERED ROAD SURFACE

A method of identifying a snow covered road includes creating a forward image of a road surface. The forward image is analyzed to detect a tire track in the forward image. When a tire track is detected in the forward image, a message indicating a snow covered road surface is signaled. When a tire track is not detected in the forward image, a rearward image, a left side image, and a right side image are created. The rearward image, the left side image, and the right side image are analyzed to detect a tire track in at least one of the rearward image, the right side image, and the left side image. A message indicating a snow covered road surface is signaled when a tire track is detected in one of the rearward image, the left side image, or the right side image. 1. A method of identifying a snow covered road surface , the method comprising:creating a forward image of a road surface in a forward region relative to a body of a vehicle, with a forward camera;analyzing the forward image, with a computing unit, to detect a tire track in the forward image;creating a rearward image of the road surface in a rearward region relative to the body of the vehicle, with a rearward camera, when a tire track is not detected in the forward image;analyzing the rearward image, with the computing unit, to detect a tire track in the rearward image; andsignaling a message indicating the road surface may be covered with snow when a tire track is detected in the rearward image.2. The method set forth in claim 1 , further comprising creating at least one of a left side image of the road surface in a left side region relative to the body of the vehicle with a left side camera claim 1 , and a right side image of the road surface in a right side region relative to the body of the vehicle with a right side camera claim 1 , when a tire track is not detected in the forward image.3. The method set forth in claim 2 , further comprising analyzing at least one of the left side image and the right side ...

Methods and systems for using artificial intelligence to evaluate, correct, and monitor user attentiveness

In an aspect, a system for using artificial intelligence to evaluate, correct, and monitor user attentiveness includes a forward-facing camera, the forward-facing camera configured to capture a video feed of a field of vision on a digital screen, at least a user alert mechanism configured to output a directional alert to a user, a processing unit in communication with the forward-facing camera and the at least a user alert mechanism, a screen location to spatial location map operating on the processing unit, and a motion detection analyzer operating on the processing unit, the motion detection analyzer designed and configured to detect, on the digital screen, a rapid parameter change, determine a screen location on the digital screen of the rapid parameter change, retrieve, from the screen location to spatial location map, a spatial location based on the screen location, and generate, using the spatial location, the directional alert.

CONSTRUCTION MACHINERY

A construction machine is equipped with a plurality of monitor cameras that capture the area surrounding a body of the machine. An image processor generates a synthetic bird's eye view image by synthesizing the plural camera images captured by each monitor camera into one. The image processor divides the synthetic bird's eye view image using one or more section lines into plural areas A, B, C, D, and expands the image size for area D corresponding to a blind spot behind and to the right side of the machine. At the same time, the image processor reduces the image size of the remaining areas A, B, C by a corresponding amount, in order to thereby generate a conversion image which is the same overall size as the synthetic bird's eye view image while maintaining a complete view of the area surrounding the machine from all of the cameras. A monitor selectively displays either the synthetic bird's eye view image or the conversion image to the operator. 1. A construction machine equipped with a plurality of monitor cameras that capture images of the area surrounding a machine body , comprising:image processing means for synthesizing the camera images captured by each of the plurality of monitor cameras into a single synthetic bird's eye view image, then dividing the aforementioned synthetic bird's eye view image into the plural sections by one or more section lines, and expanding the image size of one said section while at the same time reducing the image size among other remaining said sections to generate a conversion images of the same overall size as said synthetic bird's eye view image; anda monitor that selectively displays one of said synthetic bird's eye view image and said conversion image.2. The construction machine according to claim 1 , wherein in the center part of said synthetic bird's eye view image claim 1 , said image processing means is configured to display a predefined plan figure of the machine body in said bird's eye view image and said conversion ...

System and methods for image segmentation using convolutional neural network

The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

System and method for space object detection in daytime sky images

In some embodiments, space objects may be detected within shortwave infrared (SWIR) images captured during the daytime. Some embodiments include obtaining a stacked image by stacking shortwave infrared (SWIR) images. A spatial background-difference image may be generated based on the stacked image, and a matched-filter image may be obtained based on the spatial background-difference image. A binary mask may be generated based on the matched-filter image. The binary mask may include a plurality of bits each of which including a first value or a second value based on whether a signal-to-noise ratio (SNR) associated with that bit satisfies a threshold condition. Output data may be generated based on the spatial background-difference image and the binary mask, where the output data provides observations on detected space objects in orbit.

OCCUPANCY PREDICTION NEURAL NETWORKS

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a future occupancy prediction for a region of an environment. In one aspect, a method comprises: receiving sensor data generated by a sensor system of a vehicle that characterizes an environment in a vicinity of the vehicle as of a current time point, wherein the sensor data comprises a plurality of sensor samples characterizing the environment that were each captured at different time points; processing a network input comprising the sensor data using a neural network to generate an occupancy prediction output for a region of the environment, wherein: the occupancy prediction output characterizes, for one or more future intervals of time after the current time point, a respective likelihood that the region of the environment will be occupied by an agent in the environment during the future interval of time. 1. A method implemented by one or more data processing apparatus , the method comprising:receiving sensor data generated by a sensor system of a vehicle that characterizes an environment in a vicinity of the vehicle as of a current time point, wherein the sensor data comprises a plurality of sensor samples characterizing the environment that were each captured at different time points; the occupancy prediction output characterizes, for one or more future intervals of time after the current time point, a respective likelihood that the region of the environment will be occupied by an agent in the environment during the future interval of time; and', 'the network input is provided to an input layer of the neural network, and the occupancy prediction output for the region of the environment is output by an output layer of the neural network; and, 'processing a network input comprising the sensor data using a neural network to generate an occupancy prediction output for a region of the environment, whereinproviding the occupancy prediction output to a ...