Способ детектирования голографической защиты на документах в видеопотоке

Изобретение относится к области вычислительной техники для детектирования голографической защиты на документах в видеопотоке. Технический результат заключается в повышении надежности детектирования голографических элементов путем предотвращения ошибок ложной детекции на статичных цветных элементах документа. Для этого заявлен способ детектирования голографической защиты на документах в видеопотоке, включающий: поиск особых точек и вычисление дескрипторов на кадре; фильтрация особых точек на предыдущем кадре так, чтобы остались только точки, расположенные внутри четырехугольника внешних границ документа; сопоставление дескрипторов особых точек текущего и предыдущего кадров; применение алгоритма оценки параметров проективного преобразования между кадрами; проективное преобразование четырехугольника внешних границ документа с предыдущего кадра для получения внешних границ документа на текущем кадре; нормализация изображения документа; вычисление насыщенности и цветового тона; обновление статистик ...

Способ распознавания объектов цифровой обработкой исходных изображений в градациях серого

Изобретение относится к области цифровой обработки изображений. Способ распознавания объектов цифровой обработкой исходных изображений в градациях серого содержит этапы, на которых осуществляют наложение калибровочного набора на исходное изображение, полученное на экране ЭВМ в градациях серого с использованием электронного микроскопа, с последующим измерением интенсивности уровня серого отдельных пикселей, составляющих изображение в условных оттенках серого, и объединение между ними пикселей, имеющих одинаковую интенсивность уровня серого, в качестве калибровочного набора используют квадратную сетку с заданным числом пикселей сторон, соответствующим уровню серого в градациях серого. Технический результат – повышение информативности формирования изображения о пространственном расположении фаз микроструктуры. 7 ил.

Bewertungsverfahren für Halbleiterwafer sowie Herstellungsverfahren für Halbleiterwafer

Bereitgestellt wird ein Verfahren zur Bewertung von Halbleiterwafern, einschließend: Aufnahme eines Querschnittsbildes eines zu bewertenden Halbleiterwafers; wobei das Querschnittsbild einen Grenzbereich einschließt zwischen einer abgeschrägten Oberfläche eines äußeren Randbereichs eines Wafers und einer Waferoberfläche, welche zu der abgeschrägten Oberfläche benachbart ist; Anfertigung eines vergrößerten Bildes des aufgenommenen Querschnittsbildes durch Vergrößern des aufgenommenen Querschnittsbildes in allein der Wafer-Dickenrichtung; sowie Bewerten einer Form des Grenzbereichs im angefertigten vergrößerten Bild.

VERFAHREN ZUR AUTOMATISCHEN EXTRAKTION EINES KONTRASTIERTEN GEGENSTANDS IN EINEM DIGITALEN BILD.

Neu beleuchtbare Textur zur Verwendung beim Rendern eines Bildes

Neu beleuchtbares Rendern mit freiem Sichtwinkel erlaubt das Rendern und Neubeleuchten einer neuartigen Ansicht einer Szene auf der Basis mehrerer Ansichten der Szene aus mehreren Kamerasichtwinkeln. Eine anfängliche Textur kann in Materialien segmentiert werden, und für jedes Material wird eine anfängliche grobe Farbschätzung bestimmt. Aus den erfassten Ansichten der Szene wird Szenengeometrie geschätzt und zum Skalieren der anfänglichen groben Farbschätzungen relativ zueinander verwendet, dergestalt, dass die verschiedenen Materialien mit einer ähnlichen Bestrahlungsstärke beleuchtet zu sein scheinen. Auf diese Weise wird eine globale Bestrahlungsstärkefunktion geschätzt, die die Szenenbeleuchtung beschreibt. Dies stellt einen Ausgangspunkt für eine Farbschätzung und Schattierungsschätzungsextraktion bereit. Die Schattierungsschätzung kann zum Anpassen von Oberflächennormalen an die globale Bestrahlungsstärkefunktion verwendet werden. Die Menge von Oberflächennormalen und die Farbschätzung ...

Method and apparatus for deriving a texture stationarity confidence score

Computer based analysis of MRI images

Method for constructing prototype vectors in real time for a neural process.

Method for processing of a digital image to provide input data to wired or simulated neural processes, comprising real time pixel-by pixel reading of the image and construction of prototype vectors during said reading based on the read pixel values. The method further comprises the reading of said image through a displacing region of interest ROI (moving window) via horizontal and vertical offsets and generating an intermediate vector preliminarily to the construction of the prototype vector. Constructing the prototype vectors as the image is read achieves higher efficiency by avoiding subsequent manipulation of the same data, with better use of memory space, and reduces latencies in computation time. The invention is intended for image processes operations, such as temporal filtering, in global environmental perception and movement analysis.

Feature Aligned Interpolation Using Colour Components

A method of pixel interpolation in which a two-dimensional digital filter is arranged so as to interpolate an output pixel from a two-dimensional array of colour input pixels by aligning a filtering operation with a detected direction of an image feature at a pixel position to be interpolated. The invention comprises detecting image activity within respective colour components of a group of input pixels around the pixel position to be interpolated, generating monochromatic test pixels by combining the colour components of input pixels in relative proportions dependent upon the detected image activities and detecting the direction of an image feature in a group of the monochromatic test pixels around the pixel position to be interpolated. The output pixel is then interpolated using the two-dimensional filter, according to the detected image feature direction.

Image process with spatial periodicity measure

An image manipulation process, such as interpolation of an image in a sequence, depends on a spatial periodicity measure for image data that is a function of the sparseness of the two-dimensional spatial frequency spectrum of the processed image data. At least one two-dimensional (2D) block of image data may be processed in a two-dimensional Fourier or other spatial to frequency domain transform and preferably the periodicity measure is evaluated from the portion of the transformed image data representing AC spectrum components. Sparseness may be a linear function of the mean-square value and the square of the mean value of transformed image data for the 2D block; sparseness can further depend on the mean-square value of low spatial frequency transform output values. Sparseness can be a count of the number of spatial frequency values exceeding a threshold, and may be measured by allocating spectrum values to frequency bins, counting the number of non-zero values. The spatial periodicity ...

Automated cropping of electronic images

Double-angle gradients

Procédé de construction en temps réel de vecteurs prototypes Ó partir de données d"entrées d"un processus neuronal

DEVICE AND PROCEDURE FOR DETERMINING AN EDGE HISTOGRAM, A DEVICE AND A PROCEDURE FOR THE PLACING OF A PICTURE IN A GRAPHIC DATA BANK, A DEVICE AND A PROCEDURE FOR FINDING TWO SIMILAR PICTURES AND COMPUTER PROGRAMME

PROCEDURE AND DEVICE FOR THE SELECTION AND EXPENDITURE OF PICTURES

Method for image texture analysis

Determining palette size, palette entries and filtering of palette coded blocks in video coding

Techniques are described for palette-based coding. In palette-based coding, a video coder may form a palette as a table of colors for representing video data of a given block. Palette-based coding may be useful for coding blocks of video data having a relatively small number of colors. Rather than coding actual pixel values or their residuals for the given block, the video coder may code index values for one or more of the pixels. The index values map the pixels to entries in the palette representing the colors of the pixels. Techniques are described for determining whether to disable filtering, such as deblocking filtering or sample adaptive offset (SAO) filtering, of palette coded blocks at a video encoder or a video decoder. Techniques are also described for modify a palette size and palette entries of a palette at the video encoder based on rate-distortion costs.

Method for discriminating between malignant and benign tissue lesions

An embodiment of the present invention includes a method for discriminating between benign and malignant tissue lesions. The method includes the steps of using a plurality of maps of physiology and morphology parameters generated from reflectance measurements and pure morphology parameters generated from reflectance measurements. The method also includes calculating entropies and cross entropies of the plurality of maps, and calculating a plurality of pure morphology parameters. Further, the method includes assigning a weight to each entropy and a weight to a logarithm of each entropy, a weight to each cross entropy and a weight to a logarithm of each cross entropy, and a weight to each pure morphology parameter and a weight to a logarithm of each pure morphoiogy parameter. The method further includes computing a diagnostic index, defining a cost function, defining a proper threshold value for a diagnostic index and solving an optimization problem to determine a set of weights from the ...

Multi-stage liveness determination

A method of determining whether a biometric object is part of a live individual is described. In one such method, image information is acquired from the biometric object by using a sensor, such as an ultrasonic sensor. The image information may be analyzed in at least two analysis stages. One of the analysis stages may be a temporal analysis stage that analyzes changes in the image information obtained during a time period throughout which the biometric object was continuously available to the sensor. For example, a dead/alive stage may analyze differences between image information taken at two different times in order to identify changes from one time to the next. Other stages may focus on aspects of a particular image information set, rather than seeking to assess changes over time. These other stages seek to determine whether an image information set exhibits characteristics similar to those of a live biometric object.

BINNED MICRO-VESSEL DENSITY METHODS AND APPARATUS

In one aspect, a method of obtaining micro-vessel density (MVD) measureme nts from an image of biological vasculature containing a plurality of vessel s is provided. The method comprises acts of analyzing a region of interest o f the image for each of a plurality of bins, each of the plurality of bins a ssociated with a predetermined range of vessel sizes, the act of analyzing t he region of interest including determining which of the plurality of bins t hat portions of any vessel subject matter identified in the region of intere st belong based on a size associated with the respective portions of the ves sel subject matter, and associating each portion of the vessel subject matte r with the corresponding one of the plurality of bins to which the portion b elongs, and computing at least one measurement for each of the plurality of bins, the at least one measurement related to the MVD of the portions of ves sel subject matter associated with the respective bin.

METHOD AND APPARATUS FOR AUTOMATIC QUANTITATIVE MEASUREMENT OF TEXTURES BY IMAGE ANALYSIS

There is disclosed a method and an apparatus for automatic quantitative measurement of textures by image analysis for measuring textures of a material containing various optically anisotropic textures. An image of the material is divided into a plurality of sections and brightness of each section is classified into a plurality of stages to be stored as gray levels of the sections or pixels. Image patterns of the textures are recognized on the basis of gray level variations observed before and after a mask movement, and the textures of the material are determined according to predetermined criteria.

SYSTEMS AND METHODS FOR CHARACTERIZING WOOD FURNISH

A system for determining at least one characteristic of wood furnish from an upstream source. The system includes: an inclined panel comprising a transparent wind ow having a top surface for the wood furnish to slide down; a lighting means adjacent the transparent window for lighting the wood furnish visible through a bottom surface of the window; an image capturing means adjacent the transparent window for capturing an image of the wood furnish visible through the bottom surface of the windo w; and a processing means in communication with the image capturing means for derivin g from the captured images the at least one characteristic of the wood furnish. ...

METHOD, APPARATUS AND COMPUTER PROGRAM FOR ANALYSING MEDICAL IMAGE DATA

Medical image data is analysed to produce a biomarker. The data is filtered with a plurality of band-pass filters each having a different bandwidth. A texture parameter is then determined from the filtered data from each filter and the biomarker is determined as at a ratio of the texture parameters. When the biomarker is obtained from a CT image of a liver, it can be predictive of poor survival, disease extent and liver physiology of a patient following resection of colorectal cancer. When obtained from a mammographic image, the biomarker can be indicative of cancer invasion and receptor status within mammographic abnormalities. When obtained from a CT image of a lung nodule, the biomarker can be predictive of tumour stage (or grading) and tumour metabolism of a patient with non-small cell lung carcinoma (lung cancer). When obtained from an MRI image of the brain, the biomarker can be indicative of schizophrenia and/or other brain disorders.

Hybrid machine learning systems

PROCEEDED WITH IMAGE PROCESSING INCLUDING/UNDERSTANDING the POSTING OF AREAS Of INTEREST IN a RADIOLOGICAL IMAGE ACCORDING TO SCORES ASSOCIATE WITH the KNOWN AS AREAS

La présente invention concerne un Système de traitement d'image pour la localisation de régions potentiellement cancéreuses dans au moins une image radiologique, le système comprenant des moyens de détection d'au moins une région d'intérêt, caractérisé en ce que le système comprend : - des moyens de détection pour détecter au moins une région d'intérêt dans l'image radiographique, - des moyens d'extraction pour extraire au moins une caractéristique de chaque région d'intérêt, - des moyens d'association pour associer un score statistique à chaque région d'intérêt en fonction de la caractéristique extraite, - des moyens de réception pour recevoir une valeur définie par l'utilisateur d'un paramètre de seuillage, - des moyens de comparaison pour comparer le score de chaque région d'intérêt à la valeur du paramètre de seuillage, des moyens d'affichage (30) pour afficher la région d'intérêt en fonction du résultat de la comparaison du score à la valeur du paramètre de seuillage.

METHOD OF MEASURING THE DIRECTION OF LINEAR ELEMENTS AND DISPOSED PARALLEL TO ONE ANOTHER

Le procédé de mesure de la direction de motifs d'éléments linéaires et parallèles entre eux comprend l'exécution, au moins trois fois à la suite, de la série d'étapes suivantes - délimiter une portion d'une image comprenant les motifs, - appliquer au moins un filtre à la portion de l'image, - déterminer une valeur d'un paramètre de la portion filtrée, constituant une estimation d'un alignement entre les différents motifs, et - enregistrer la valeur déterminée, puis, après cette exécution, comparer les valeurs enregistrées et déterminer une valeur maximale du paramètre.

DEVICE FOR DETECTING STATE OF THE CORNEA OF THE EYE AND ASSOCIATED METHOD

L'invention concerne un dispositif (1) de détection d'un état de la cornée (3) de l'œil (5) comprenant une unité (11) d'imagerie de contraste à granularité laser, présentant • une source (13) de rayonnement électromagnétique cohérent destiné à éclairer la cornée (3) de l'œil (5), • un détecteur (15) d'au moins une image de chatoiement diffusée par la cornée (3) de l'œil (5), • une unité (17) de traitement de l'image de chatoiement configurée pour établir un histogramme normalisé de l'image de chatoiement, et pour déterminer l'ordre de la loi Gamma permettant d'extrapoler l'histogramme normalisé.

METHOD FOR DETECTING DEFECTS ON A TIRE SURFACE

METHOD AND SYSTEM FOR PROCESSING MULTIPLE SERIES OF BIOLOGICAL IMAGES OBTAINED FROM A PATIENT

불균일 검사 시스템, 불균일 검사 방법 및 불균일 검사 프로그램

... 불균일 검사 시스템은, 검사 대상의 촬상 화상을 취득하기 위한 촬상부와, 촬상 화상에 기초하여 색 불균일 검사용 화상 및 휘도 불균일 검사용 화상을 각각 생성하는 화상 생성부와, 색 불균일 검사용 화상 및 휘도 불균일 검사용 화상의 양쪽을 사용해서 평가 파라미터를 산출하는 산출부와, 산출된 평가 파라미터를 사용해서 불균일 검사를 행하는 검사부를 구비하고 있다. 화상 생성부는, 촬상 화상에 대하여 색 성분과 휘도 성분의 화상 분리 처리를 행함으로써, 색 성분 화상 및 휘도 성분 화상을 각각 생성함과 함께, 색 성분 화상 및 휘도 성분 화상에 대하여 시각의 공간 주파수 특성을 고려한 필터 처리를 개별로 행하고, 필터 처리 후의 색 성분 화상 및 휘도 성분 화상에 기초하여 색 불균일 검사 화상 및 휘도 불균일 검사용 화상을 각각 생성한다. 산출부는, 색 및 휘도의 양쪽에 대한 불균일 시감도를 고려하여, 평가 파라미터를 산출한다.

ANGLE DEPENDENT ANISOTROPIC FILTERING

SYSTEM FOR PREDICTING OCCURRENCE OF DEFECTIVE IMAGE AND PROGRAM FOR PREDICTING OCCURRENCE OF DEFECTIVE IMAGE

A system for predicting occurrence of a defective image includes: an input device configured to input image data into an image forming apparatus; and a hardware processor configured to analyze a spatial frequency of gradient distribution of an image in accordance with a size of a density irregularity specific to the image forming apparatus with respect to the input image data and to calculate a probability of a conspicuous density irregularity of the size in regard to the image formed by the image forming apparatus based on the image data with reference to an index of correlation between an analysis result and an evaluation value of the density irregularity.

System and method for quantification of size and anisotropic structure of layered patterns

Parameterization of incremental patterns of various categories is provided by a computer system. The computer system initially undergoes filtering of the incremental patterns under study. Transects are plotted in a predetermined direction to growth incremental bands, and converted into an anisotropic structure in a 2-D domain. The width of the incremental bands along transects are calculated in combination with the area of incremental bands between neighboring transects. The structure of the incremental bands along with the width and area of the incremental hands across a 2-D plane for different levels of noise are calculated. Noise is reduced by averaging width and area across the 2-D plane. Indices of adequacy of the model and structural anisotropy of the incremental patterns are calculated.

Image filtering apparatus and method

PCT No. PCT/GB85/00580 Sec. 371 Date Aug. 12, 1986 Sec. 102(e) Date Aug. 12, 1986 PCT Filed Dec. 12, 1985 PCT Pub. No. WO86/03594 PCT Pub. Date Jun. 19, 1986.In an image processing system for elimination of speckle a signal derived from a scan is passed to a statistical processor (21). An output characteristic of a statistical parameter of the local value of this image signal is passed to a comparator (4) and compared with a reference characteristic of speckle to derive a control signal for a variable bandwidth filter (6) used to enhance the image.

Medical image processing apparatus and method for detecting locally protruding lesion

A medical image processing apparatus of the present invention includes: a three-dimensional model estimating section for estimating a three-dimensional model of an object based on a two-dimensional image of an image of the object which is inputted from a medical image pickup apparatus; an image dividing section for dividing the two-dimensional image into a plurality of regions each of which includes at least one or more pixels; a feature value calculation section for calculating a feature value according to a grayscale of each pixel in one region for each of the plurality of regions; and a lesion detection reference setting section for setting lesion detection reference for detecting a locally protruding lesion in the regions of the three-dimensional model which correspond to each of the plurality of regions, based on the feature value according to the grayscale.

ACCESSION NUMBER CORRECTION SYSTEM AND METHODS FOR USE THEREWITH

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

Characterizing wood furnish by edge pixelated imaging

A system for determining at least one characteristic of wood furnish from an upstream source. The system includes: an inclined panel comprising a transparent window having a top surface for the wood furnish to slide down; a lighting means adjacent the transparent window for lighting the wood furnish visible through a bottom surface of the window; an image capturing means adjacent the transparent window for capturing an image of the wood furnish visible through the bottom surface of the window; and a processing means in communication with the image capturing means for deriving from the captured images the at least one characteristic of the wood furnish.

IMAGE ANTI-ALIASING METHOD AND IMAGE ANTI-ALIASING DEVICE

An image anti-aliasing method comprises following steps: receiving an aliasing image; wherein the aliasing image includes a plurality of source pixels; generating a plurality of hash values corresponding to the plurality of source pixels respectively; and performing a filtering processing or a filter generating procedure on the aliasing image according to the plurality of hash values. Each of the plurality of hash values is generated by the following steps: selecting one source pixel from the aliasing image and selecting a window containing the selected source pixel; determining an aliasing strength according to the grayscale values of all the source pixels of a row and a column at which the selected source pixel is located in the window; and determining a hash value of the selected source pixel according to the aliasing strength.

Systems and Methods for Hybrid Depth Regularization

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

DETECTION TARGET POSITIONING DEVICE, DETECTION TARGET POSITIONING METHOD, AND SIGHT TRACKING DEVICE

Disclosed is a detection target positioning method and device. The method comprises: acquiring an original image and pre-processing the original image to obtain a gradation of each pixel in a target gradation image corresponding to a target region including a detection target; calculating first gradation sets corresponding to rows of pixels of the target gradation image and second gradation sets corresponding to columns of pixels of the target gradation image; and determining rows of two ends of the detection target in a column direction according to the first gradation sets, determining columns of two ends of the detection target in a row direction according to the second gradation sets, and determining a center of the detection target according to the row of two ends of the detection target in the column direction and the columns of two ends of the detection target in the row direction.

APPARATUS AND METHOD OF ACQUIRING IMAGE BY EMPLOYING COLOR SEPARATION LENS ARRAY

Provided is an apparatus for acquiring images including an image sensor including a sensor substrate including a plurality of photo-sensing cells sensing light, and a color separation lens array provided above the sensor substrate, the color separation lens array including a fine structure in each of a plurality of regions respectively facing the plurality of photo-sensing cells and separating incident light based on color, the fine structure forming a phase distribution to condense light having different wavelengths on adjacent photo-sensing cells, a signal processor configured to perform, based on a point spread function corresponding to each color pixel by the color separation lens array, deconvolution on sensing signals of the plurality of photo-sensing cells to process an image signal for each color obtained by the image sensor, and an image processor configured to form a color image from the image signal for each color processed by the signal processor. 1. An apparatus for acquiring images , the apparatus comprising: a sensor substrate comprising a plurality of photo-sensing cells configured to sense light; and', 'a color separation lens array provided above the sensor substrate, the color separation lens array comprising a fine structure in each of a plurality of regions respectively facing the plurality of photo-sensing cells and configured to separate incident light based on color, the fine structure forming a phase distribution to condense light having different wavelengths on adjacent photo-sensing cells;, 'an image sensor comprisinga signal processor configured to perform, based on a point spread function (PSF) corresponding to each color pixel by the color separation lens array, deconvolution on sensing signals of the plurality of photo-sensing cells to process an image signal for each color obtained by the image sensor; andan image processor configured to form a color image from the image signal for each color processed by the signal processor.2. The ...

Dual sensor imaging system and depth map calculation method thereof

A dual sensor imaging system and a depth map calculation method thereof are provided. The dual sensor imaging system includes at least one color sensor, at least one infrared ray (IR) sensor, a storage device, and a processor. The processor is configured to load and execute a computer program stored in the storage device to: control the color sensor and the IR sensor to respectively capture multiple color images and multiple IR images by adopting multiple exposure conditions suitable for an imaging scene, adaptively select a combination of the color image and the IR image that are comparable to each other from the color images and the IR images; and calculate a depth map of the imaging scene by using the selected color image and IR image.

СПОСОБ И УСТРОЙСТВО ДЛЯ ОБНАРУЖЕНИЯ ИГРОВЫХ ЭПИЗОДОВ В ПОЛЕВЫХ ВИДАХ СПОРТА В ВИДЕОПОСЛЕДОВАТЕЛЬНОСТЯХ

Изобретение относится к технологиям обработки видеоизображений, а именно к автоматизации процесса классификации видеоконтента по жанру и содержанию в реальном режиме времени. Техническим результатом является автоматическая подстройка настроек изображения. Результат достигается тем, что вычисляют яркость и насыщенность каждого пикселя кадра, описываемого значениями в красном, зеленом и синем цветовых каналах; вычисляют абсолютную величину градиента яркости для каждого пикселя кадра; выполняют классификацию по цвету каждого пикселя кадра; вычисляют статистику по результатам классификации по цветам для всего кадра; вычисляют статистику по результатам классификации по цветам для зеленых областей кадра; определяют, является ли данный кадр игровым эпизодом в полевых видах спорта, на основании исключительно характеристик текущего кадра; определяют, принадлежит ли текущий кадр той же сцене, что и предыдущий кадр; используют результат детектирования, полученный для предыдущего кадра, в качестве ...

Идентификация или определение груза на основе текстуры

Изобретение относится к способу и устройствам для контроля груза в контейнере. Техническим результатом является повышение точности классификации груза в контейнере. Способ контроля груза в контейнере включает: классификацию одного или более фрагментов оцифрованного изображения, при этом оцифрованное изображение создают системой контроля, выполненной с возможностью контролировать контейнер путем пропускания излучения из источника излучения к детектору излучения через контейнер, где классификация содержит: извлечение одного или более текстурных дескрипторов (V, Р) фрагмента и классификацию фрагмента путем сравнения одного или более извлеченных текстурных дескрипторов (V, Р) фрагмента с соответствующими одним или более эталонными текстурными дескрипторами (Vr, Wr, Рг), относящимися к соответствующим одному или более классам эталонных видов товаров, причем один или более эталонных текстурных дескрипторов (Vr, Wr, Рг) извлекают из одного или более эталонных изображений одного или более эталонных ...

ОБРАБОТКА ДАННЫХ ДЛЯ СВЕРХРАЗРЕШЕНИЯ

Изобретение относится к области цифровой обработки изображений и видео. Технический результат – улучшение качества изображения и видео без потери данных изображений. Устройство обработки данных для сверхразрешения содержит блок оценки смещения, выполненный с возможностью принимать набор изображений низкого разрешения одной сцены, получать наборы смещений пикселей для изображений низкого разрешения для всех пикселей, соответствующих одним и тем же фрагментам в наборе изображений низкого разрешения, получать наборы целочисленных смещений пикселей посредством вычисления целочисленного смещения пикселя для каждого смещения пикселя, получать наборы дробных смещений пикселей посредством вычисления дробного смещения пикселя для каждого смещения пикселя; банк фильтров, выполненный с возможностью хранения наборов фильтров; блок выбора фильтров; блок получения изображения высокого разрешения, выполненный с возможностью получения изображения высокого разрешения в формате RGB. 2 н. и 16 з.п. ф-лы, ...

СПОСОБ РАСПОЗНАВАНИЯ ГРАФИЧЕСКИХ ОБРАЗОВ ОБЪЕКТОВ

Изобретение относится к области цифровой обработки изображений. Технический результат – повышение скорости и точности распознавания графических образов при одновременном уменьшении количества ложных распознаваний. Способ распознавания графических образов объектов на исходном изображении, представленном в цифровом виде в градациях серого, инвариантный к поворотам и масштабированию графических образов объектов на изображении, характеризуется предварительным формированием эталонных контуров и эталонных фрагментов контуров, выполнением предварительной обработки исходного изображения, выделением и замыканием контуров графических образов объектов исходного изображения, переводом полученных контуров из пространства координат растра в комплекснозначное пространство вектор-контуров, выполнением структурного анализа формы изображения фильтрами, используя вычисление значения модуля нормированного скалярного произведения и строя взаимно корреляционной функции контуров графических образов объектов исходного ...

Способ детектирования голографических элементов в видеопотоке

Изобретение относится к области вычислительной техники и может быть использовано для обнаружения и распознавания голограмм в видеопотоке. Технический результат заключается в повышении точности определения голографических элементов, содержащихся в документе, который представляет собой изображение видеопотока. Способ определения голограмм в видеопотоке, содержащем изображения в виде документов, заключается в том, что осуществляют обработку видеопотока, при которой выполняют стабилизацию документа, далее осуществляют построение карт насыщенности и цветового тона, после чего проводят анализ цветовых характеристик в областях изображения, далее осуществляют построение гистограмм цветовых характеристик, с учетом полученных данных осуществляют оценку изменения цветовых характеристик путем вычисления разницы между гистограммами на текущем и предыдущем кадрах, с учетом оценки изменения цветовых характеристик осуществляют построение интегральной карты оценок присутствия голограмм путем объединения ...

Vorrichtung und Verfahren zum Bestimmen eines Kanten-Histogramms, Vorrichtung und Verfahren zum Ablegen eines Bildes in einer Bilddatenbank, Vorrichtung und Verfahren zum Auffinden von zwei ähnlichen Bildern und Computerprogramm

Verfahren zum Ermitteln von Gewebeeigenschaften von Tumoren

Es wird ein Verfahren zum Ermitteln von Gewebeeigenschaften in einem Untersuchungsbereich beschrieben. Bei dem Verfahren werden kontrastmittelgestützte Projektionsmessdaten (PMD1, PMD2, PMD3) von dem Untersuchungsbereich akquiriert, welche mindestens zwei spektrale Projektionsmessdatensätze (PMD1, PMD2) umfassen. Es werden weiterhin Bilddaten (BD1, BD2, BD3) auf Basis der akquirierten Projektionsmessdaten (PMD1, PMD2, PMD3) rekonstruiert, wobei die Bilddaten (BD1, BD2, BD3) mindestens zwei spektrale Bilddatensätze (BD1, BD2) umfassen. Anschließend werden Texturparameter (TP1, TP2, TP3) auf Basis der rekonstruierten Bilddaten (BD1, BD2, BD3) ermittelt und es wird eine Parameteranalyse auf Basis der Parameter-Datenbasis durchgeführt. Weiterhin wird eine Bildanalyseeinrichtung (20) beschrieben. Überdies wird ein Computertomographiesystem (40) beschrieben.

GERAET UND VERFAHREN ZUM FILTERN EINER ABBILDUNG.

Sensor data processing

Method for describing texture or a texture-like region

A method for characterising texture or a texture-like region in an image includes the steps of obtaining saliency values (104) of an image or set of images and applying a threshold to the saliency values (108), to remove the less salient features. A three dimensional shape, for example a cuboid of a predefined size, is generated (210) and saliency space sampled by moving the cuboid across spatial dimensions of the saliency space. An estimation of a probability density function of scales within that sample space is generated and texture or a texture-like region in the saliency space is characterised using the estimation. This provides a method by which texture can be classified within an image to aid image interpretation. In particular, the texture is classified independent of scale, orientation and illumination. The method is particularly useful for texture classification problems where: the scale is unknown, the scale may vary, or a general scene description is required.

Method and apparatus for analysing image data

Image processing

DEVICE AND PROCEDURE FOR DETERMINING AN EDGE HISTOGRAM, A DEVICE AND A PROCEDURE FOR THE PLACING OF A PICTURE IN A GRAPHIC DATA BANK, A DEVICE AND A PROCEDURE FOR FINDING TWO SIMILAR PICTURES AND COMPUTER PROGRAMME

VERFAHREN ZUR EXTRAKTION VON KANTENSEGMENTEN

System and method for filtering a medical image

Texture advisor

BANDING ARTIFACT DETECTION IN DIGITAL VIDEO CONTENT

A method and system for identifying and determining banding artifacts in digital video content composed of a sequence of moving video pictures includ es creating a mask image (402) corresponding to a picture from said sequence of moving video pictures based on global gradient changes to detect potenti al areas containing banding artifacts. The values of the mask image are scal ed (404) thereby making banding artifact detection possible using gradient o perators. The banding artifacts are then identified / detected (406) based o n the local gradients.

REPRODUCIBLE QUANTIFICATION OF BIOMARKER EXPRESSION

A method is described for the reproducible quantification of biomarker expression, including biomarker expression in a tissue sample. Methods and systems are described whereby reproducible scores for biomarker expression are obtained independent of instrument, its location, or operator.

VISUAL SEGMENTATION OF LAWN GRASS

This invention provides a method for identifying lawn grass comprising capturing an image of the terrain in front of a mower, segmenting the image into neighborhoods, calculating at least two image statistics for each of the neighborhoods, generating a binary representation of each image statistic. The binary representation of each image statistic is generated by comparing the calculated image statistic values to predetermined image statistic values for grass. The method further comprises weighting each of the binary representations of each image statistic, and summing corresponding neighborhoods for all image statistics. A binary threshold is applied to each of the summed neighborhoods to generate a binary map representing grass containing areas and non-grass containing areas.

METHOD AND APPARATUS FOR MEASURING CRIMP FREQUENCY OF A WEB

... 2087040 9202001 PCTABS00010 A method and apparatus (12) to non-destructively inspect and quantify yarn crimp characteristics (10) either off-line or on-line at speeds in excess of 2,000 yds./min. by acquiring images of yarn crimp on-line during the manufacturing process, then digitizing the images of yarn crimp and performing all data processing from the digital data required. Each image is segregated into features (e.g. valid crimp, low frequency oscillations, high frequency oscillations, edge oscillations, and unconfirmed oscillations) with each parameter being analyzed separately. Features are segmented two dimensionally using techniques such as, but not limited to, local max/min intensity profiles and spatial intensity and frequency thresholding. Regions identified as abnormal are separated from areas of valid crimp before determining crimp frequency. Outputs of individual analysis are combined to determine global tow quality. This method provides a complete statistical analysis for ...

Content file image analysis

Method for detecting state of focusing

UNSUPERVISED METHOD FOR SEGMENTATION OF A TEXTURE.

Windscreen rain drop detection system uses autocorrelation analysis of video image contrast distribution to distinguish obscuration type and start suitable clearing action

On propose un procédé et un dispositif pour la détection d'objets se trouvant sur la surface (9a) d'un élément transparent (9) avec un détecteur au moins partiellement focalisé sur la surface (9a) avec plusieurs champs de détection pour déterminer une découpe prédéterminée de la surface (9a) ainsi qu'une unité électronique (7) pour l'exploitation de signaux du détecteur (6). L'unité électronique (7) est adaptée pour pouvoir évaluer, sur la base de la répartition de contraste d'une image d'au moins une partie de la découpe, la surface correspondante en fonction des objets se trouvant sur celle-ci.

PROCESS OF IMAGE PROCESSING FOR the ESTIMATE Of a RISK OF RUPTURE OF PLATE Of ATHEROME

La présente invention concerne un procédé de traitement d'images pour l'estimation d'un risque de rupture d'une plaque d'athérome comprenant une étape de réception (40) d'une image, dite « élastogramme », représentant les déformations internes qui résultent de la compression d'un tissu vasculaire sanguin analysé en fonction de la pression sanguine, une étape de pré-segmentation (50) de l'élastogramme pour obtenir une image pré-segmentée comprenant une pluralité de zones, et une étape de calcul d'une image d'élasticité (60) représentant l'élasticité d'au moins une région de l'élastogramme, la région correspondant à une zone sélectionnée parmi la pluralité de zones de l'image pré-segmentée, et l'image d'élasticité permettant à l'utilisateur d'estimer le risque de rupture de plaque d'athérome, ce procédé étant remarquable en ce que : - d'une part la pré-segmentation est réalisée en considérant le tissu comme étant un solide élastique incompressible de manière à déterminer le gradient du module ...

SYSTEMS AND METHODS FOR DETECTING DEFECTS IN PRINTED SOLDER PASTE

determinação de tamanho de paleta, entradas de paleta e filtragem de blocos codificados por paleta em codificação de vídeo

Texture detection in image processing

An embodiment relates to a method for the detection of texture of a digital image, including providing a raw data image of the image by means of Bayer image sensors, determining noise in at least a region of the raw data image and determining the texture based on the determined noise without using a high pass or low pass filter.

Systems and methods for detecting defects in printed solder paste

A method of analyzing an image of a substance deposited onto a substrate, the image comprising a plurality of pixels, includes defining a region of interest in the image, associating the region of interest with first and second perpendicular axis, wherein a set of pixels in the image lie along the first axis, converting the pixels in the region of interest to a single dimensional array aligned with the first axis and projecting along the second axis, and applying at least one threshold to the single dimensional array, the threshold based at least in part on a predetermined limit.

Camera-level image processing

Devices, systems and methods are disclosed for performing image processing at a camera-level. For example, a camera service may run on top of a camera hardware abstraction layer (HAL) and may be configured to perform image processing such as applying a blurring algorithm, applying a color filter and/or other video effects. An application may pass metadata to the camera service via an application programming interface (API) and the camera service may use the metadata to determine parameters for the image processing. The camera service may apply the blurring algorithm for a first period of time before transitioning to unblurred image data over a second period of time.

METHOD AND SYSTEM FOR DETECTING DISORDERS IN RETINAL IMAGES

The present disclosure discloses a method and system for detecting disorders in retinal images. The method comprises, receiving one or more retinal images. Then, identifying one or more gross pathologies and extracting one or more patches around the one or more gross pathologies. Further, assigning confidence value to each of the one or more patches and classifying each of the one or more patches as belonging to a label of the set of labels. Further, computing a histogram for each label of the set of labels. Further, generating, a confidence vector for the corresponding retinal image. Further, generating a feature vector by combining the confidence vector generated for each of the one or more retinal images. A value of the feature vector determines the presence and grade of disorder. 1. A method for detecting disorders in retinal images , comprising:receiving, by a disorder detection system, one or more retinal images;identifying, by the disorder detection system, one or more gross pathologies in each of the one or more retinal images, wherein each of the one or more gross pathologies is associated with a corresponding set of labels;extracting, by the disorder detection system, one or more patches based on each of the one or more gross pathologies in a corresponding retinal image of the one or more retinal images;assigning, by the disorder detection system, a confidence value to each of the one or more patches in the corresponding retinal image of the one or more retinal images, for indicating a probability of each of the one or more patches belonging to each label of the corresponding set of labels;classifying, by the disorder detection system, each of the one or more patches, into a label from the corresponding set of labels based on the corresponding confidence value;generating, by the disorder detection system, a confidence histogram for each of the classified labels for the corresponding retinal image of the one or more retinal images, wherein the confidence ...

Method for automated analysis of apical four-chamber images of the heart

A method for quantitatively analyzing digital images of approximately elliptical body organs, and in particular, echocardiographic images is provided. In particular, methods are disclosed for obtaining short-axis apical four-chamber views of a heart, and particularly for obtaining high-quality automated images of particular regions of the heart muscle, as viewed along its long axis.

COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR IMAGE TEXTURE DETERMINATION

This disclosure relates generally to image processing, and more particularly to image texture determination. In one embodiment a processor a memory coupled to the processor, wherein the processor coupled with a plurality of modules stored in the memory: At least one image having a plurality of pixels is accepted. Any noise is removed from the image to obtain at least one noise free image. The at least one noise free image is converted to at least one gray scale image. Horizontal and Vertical Gradient for plurality of pixels of the at least one gray scale image are computed. Gradient magnitude is calculated for the generated gradient. Histogram of the gradient magnitude is generated based on the gradient magnitude, and the plurality of generated histograms are compared with a plurality of predetermined histograms.

СПОСОБ ПРЕОБРАЗОВАНИЯ ДЕСКРИПТОРА ИЗОБРАЖЕНИЯ НА ОСНОВЕ ГИСТОГРАММЫ ГРАДИЕНТОВ И СООТВЕТСТВУЮЩЕЕ УСТРОЙСТВО ОБРАБОТКИ ИЗОБРАЖЕНИЙ

Изобретение относится к области вычисления дескрипторов изображения. Технический результат – обеспечение уменьшения размера дескриптора изображения посредством преобразования. Способ для преобразования дескриптора изображения в преобразованный дескриптор изображения содержит этапы, на которых: принимают дескриптор изображения на основе гистограммы (h) градиентов, причем гистограмма градиентов содержит множество компонент (h) гистограммы, содержащих первый набор значений, которые вычисляются в выборочных точках изображения в области около ключевой точки изображения и суммируются в гистограммы ориентаций, суммирующие содержание по подобластям изображения; применяют критерий местоположения упомянутых компонент гистограммы для преобразования упомянутой гистограммы градиентов, причем упомянутый критерий местоположения предоставляется для вычисления второго набора значений, представляющих выборочные точки изображения около упомянутой ключевой точки изображения, извлеченной из компонент гистограммы ...

Способ устранения избыточности при сжатии последовательности изображений

Graphics texture mapping

Relightable texture for use in rendering an image

ADAPTIVE FILTERING

Graphics texture mapping

Sampling a texture using anisotropic filtering, comprising: for a sampling position in the texture having coordinates x and y, determining the square root of the coefficient F for an ellipse defined by the equation Ax2 + Bxy + Cy2 = F, the ellipse corresponding to the projection of the sampling position on to the surface to which the texture is applied, and using the square root of F to determine a number of positions to sample the texture along an anisotropy direction; taking the samples and using them to determine an output sampled texture value for the sampling position. Also disclosed: using mipmaps to represent the texture at different levels of detail and using a log2 operation on the square root of F to determine the level of detail at which to sample the texture. Also disclosed: selecting between two different levels of detail. Also disclosed is determining an anisotropy direction by: determining X and Y components of a vector corresponding to the major axis of the elliptical projection ...

Graphics texture mapping

VERFAHREN ZUR EXTRAKTION VON KANTENSEGMENTEN

BINNED MICRO VESSEL DICHTEVERFAHREN AND DEVICES

PROCEDURE FOR RECOGNIZING RAINS ON A WINDSHIELD

WAS BASED DETECTION OF A MEDICAL DEVICE

System and method for filtering a medical image

Aerial image segmentation for refineries

Abstract A system receives a two-dimensional digital image of an aerial industrial plant area. Based on requirements of image processing, the image is zoomed in to different sub-images, that are referred to as first images. The system identifies circular tanks, vegetation areas, process areas, and buildings in the first image. The system formulates a second digital image by concatenating the first images. The system creates one or more polygons of the regions segmented in the second digital image. Each polygon encompasses a tank area, a vegetation area, a process area, or a building area in the second digital image, which is a concatenated image of the individual regions. The system displays the second digital image on a computer display device. INPUT IMAGE FROM LAT-LON COORDINATES OBTAIN ZOOMED IN VERSIONS OF INPUT IMAGE SEGMENT TANK FARM AREA IN THE ZOOMED IN IMAGE SEGMENT FOREST AREA IN 'TO FOREST'PIXELS SEGMENT PROCESS AREA IN 'TO PROCESS' PIXELS REMAINING AREAS ARE BUILDING AREAS NOZOOMED ...

DETERMINING POSITIONS AND ORIENTATIONS OF OBJECTS

Methods and apparatus for determining poses of objects acquire plural images of the objects from different points of view. The images may be obtained by plural cameras arranged in a planar array. Each image may be processed to identify features such as contours of objects. The images may be projected onto different depth planes to yield depth plane images. The depth plane images for each depth plane may be compared to identify features lying in the depth plane. A pattern matching algorithm may be performed on the features lying in the depth plane to determine the poses of one or more of the objects. The described apparatus and methods may be applied in bin-picking and other applications.

REPRODUCIBLE QUANTIFICATION OF BIOMARKER EXPRESSION

A method is described for the reproducible quantification of biomarker expression, including biomarker expression in a tissue sample. Methods and systems are described whereby reproducible scores for biomarker expression are obtained independent of instrument, its location, or operator.

For the organization from the Image analysis method and system

Processing system of texture images, texture analyzer and texture synthesizer

AUTOMATIC PROCESS Of EXTRACTION Of an OBJECT CONTRASTS IN an Digital image

PROCESS OF DETECTION OF RAIN ON A PAREBRISE

L'invention concerne un procédé de détection de gouttes de pluie sur un pare-brise, qui utilise une caméra qui est disposée à l'intérieur du véhicule en regard du pare-brise et qui est focalisée à l'infini. Il peut comprendre les étapes suivantes : - une étape d'acquisition d'images par une caméra à travers le pare-brise, caméra de préférence focalisée sur l'infini, - une étape d'extraction des contours des taches présentes sur les images acquises, - une étape de réalisation de l'histogramme des largeurs de contours des taches.

후보영역에서의 차량의 하단선 검출방법

... 본 발명은 후보영역에서의 차량의 하단선 검출방법에 관한 것으로, 단안 카메라로부터 밝기값 영상을 입력받아 차량의 영상을 생성하는 후보영역에서의 차량의 하단선 검출방법에 있어서, 현재 영상에서 차량이 위치한다고 판단된 영역의 이미지 좌표계의 후보 영역 사각형 정보(C(x,y), C(w), C(h))를 할당하는 제1 단계(여기서, (사각형의 상단 좌측의 x, y 좌표인 C(x,y)와, 사각형의 크기를 결정하는 C(w)(후보영역의 폭, width), C(h)(후보영역의 높이, height), 할당된 사각형의 바닥부분을 중심으로 실제 하단선(bottom line)예측을 위한 관심 영역(ROI)을 설정하되, 관심영역은 후보 영역의 높이(h)를 기준으로 후보 영역의 높이의 상하 20%를 관심 영역으로 정하며, R(x,y)(관심영역 사각형의 상단 좌측의 x,y 좌표) 및 R(w)(관심영역의 폭,width), R(h)(관심영역의 높이, height)로 표현하는 제 2단계, 설정된 관심 영역에 대하여 하단선을 예측하는 제3 단계, 하단선을 검출하기 위해서, 관심영역 내의 픽셀들의 상하의 밝기차를 구하여, 이 차이들을 누적합으로 하는 히스토그램을 구하며, 이 히스토그램에서 관심영역 내의 픽셀들의 상하의 밝기차가 큰 피크치(peak)들을 구하는 제4 단계, 계산된 히스토그램 중 피크치들에 해당하는 선들을 검출하고 차량의 하단선이 될 후보 라인들 중 값이 큰 후보 라인들을 차량의 하단선으로 예측하는 제5 단계를 포함하는 것을 특징으로 한다.

IMAGE PROCESSING APPARATUS AND METHOD, AND IMAGE PICKUP APPARATUS

Systems and methods for hybrid depth regularization

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

Image processing apparatus, image processing method and image processing program

A hemorrhage edge candidate area extraction section extracts a candidate area for the outline part of a hemorrhage area, based on an image signal of a medical image constituted by multiple color signals obtained by capturing an image of a living body. A feature quantity calculation section calculates a feature quantity of the hemorrhage area based on calculation of the amount of change in the image signal in a small area including the candidate area, among multiple small areas obtained by dividing the medical image. A hemorrhage edge determination section determines whether or not the candidate areas are the outline part of the hemorrhage area based on the feature quantity.

SYSTEM AND METHOD FOR DETECTING THE AUTHENTICITY OF PRODUCTS

System and method for detecting the authenticity of products by detecting a unique chaotic signature. Photos of the products are taken at the plant and stored in a database/server. The server processes the images to detect for each authentic product a unique authentic signature which is the result of a manufacturing process, a process of nature etc. To detect whether the product is genuine or not at the store, the user/buyer may take a picture of the product and send it to the server (e.g. using an app installed on a portable device or the like). Upon receipt of the photo, the server may process the receive image in search for a pre-detected and/or pre-stored chaotic signature associated with an authentic product. The server may return a response to the user indicating the result of the search. A feedback mechanism may be included to guide the user to take a picture at a specific location of the product where the chaotic signature may exist. 129-. (canceled)30. A memory device having recorded thereon non-transitory computer readable instructions for installing on a portable computing device comprising an image capturing device , the instructions when executed by a processor causes the computing device to:capture an image of a given product;send the image of the given product to a remote server for verification;receive from the remote server a request to take a close-up image of the given product, and location information for a region of interest (ROI) for the close-up image;executing the request to guide a user towards the ROI to take the close-up image.31. The device of claim 30 , wherein executing the request comprises:displaying a visual indicator on a display device associated with the computing device to guide the user towards the ROI to take the close-up image.32. The device of claim 31 , wherein the visual indicator includes direction information to guide the user in a given direction.33. The device of claim 31 , wherein the visual indicator includes an ...

Display apparatus and a method therefor

A display apparatus for presenting an image comprises an image receiver ( 101 ) for receiving an image to be displayed. An image analyzer ( 103 ) performs a local image profile analysis on at least a first region of the image to determine a pixel value spatial variation characteristic. The image analyzer ( 103 ) is coupled to a scaling processor ( 105 ) which scales at least a second region of the image in response to the pixel value spatial variation characteristic. The scaling processor ( 105 ) is coupled to a presentation controller ( 107 ) which presents the scaled image. The scaling may specifically be adjusted dependent on a sharpness or spatial frequency characteristics of the image. The invention may allow an improved adaptation of the presentation of one or more images to the specific characteristics of the image(s).

Texture Identification

Technologies are generally described for determining a texture of an object. In some examples, a method for determining a texture of an object includes receiving a two-dimensional image representative of a surface of the object, estimating a three-dimensional (3D) projection of the image, transforming the 3D projection into a frequency domain, projecting the 3D projection in the frequency domain onto a spherical co-ordinate system, and determining the texture of the surface by analyzing spectral signatures extracted from the 3D projection on the spherical co-ordinate system.

Method for quantifying the development of pathologies involving changes in the volumes of bodies, notably tumors"

A method for quantifying the development of pathologies involving changes in volume of a body represented via an imaging technique, including normalizing gray levels by a midway technique for two images I 1 and I 2 representing the same scene, resulting in two normalized images I′ 1 and I′ 2 ; calculating a map of signed differences between the two normalized images I′ 1 and I′ 2 ; and performing one or more statistical tests based on the assumption of a Gaussian distribution of the gray levels for healthy tissues in the normalized images I′ 1 and I′ 2 and/or in the calculated difference map. Advantageously, results of two or more of the tests can be combined for a more specific characterization of the development.

Method and system for describing image region based on color histogram

A method and system for describing image region based on color histogram is provided. The method for describing an image region based on color histogram comprising: a color quantization step for quantizing said image region into a quantized color region, wherein a specific color range and colors outside the specific color range are differently treated in the quantizing; a color histogram calculation step for computing one or more color histograms according to said quantized color region; and a histogram assembling step for generating a unique vector by using said one or more color histograms.

Device, system and method for automatic detection of contractile activity in an image frame

A device, system and method for automatic detection of contractile activity of a body lumen in an image frame is provided, wherein image frames during contractile activity are captured and/or image frames including contractile activity are automatically detected, such as through pattern recognition and/or feature extraction to trace image frames including contractions, e.g., with wrinkle patterns. A manual procedure of annotation of contractions, e.g. tonic contractions in capsule endoscopy, may consist of the visualization of the whole video by a specialist, and the labeling of the contraction frames. Embodiments of the present invention may be suitable for implementation in an in vivo imaging system.

Computationally Efficient Method for Image Segmentation with Intensity and Texture Discrimination

A computationally efficient image segmentation method is provided that processes a grayscale digital image to more clearly show textures in the underlying object shown in the digital image. A grayscale digital image is converted to an intensity matrix based on the brightness of the pixels in the image, where each matrix element represents a pixel in the digital image and has a value corresponding to the intensity, i.e., the brightness, of that pixel. The value of each matrix element is compared to the value of its nearest neighbor matrix element, and the pixel represented by the matrix element is categorized as being “dark” or “bright” based on its value, and is categorized as being “smooth” or “rough” based on the values of the nearest neighbor matrix elements. As each pixel is categorized, it is assigned a shading level corresponding to the brightness/texture matrix element value. A processed image having only the assigned shading levels is then produced, with the processed image indicating textures of the underlying object shown in the original grayscale digital image.

Soybean cultivar

The present invention is in the field of soybean variety EC1661152, EC1661470, EC1661291, AND/OR EC1661249 breeding and development. The present invention particularly relates to the soybean variety EC1661152, EC1661470, EC1661291, AND/OR EC1661249 and its seed, cells, germplasm, plant parts, and progeny, and methods of using EC1661152, EC1661470, EC1661291, AND/OR EC1661249 in a breeding program.

AUTOMATIC PATIENT RECRUITMENT SYSTEM AND METHODS FOR USE THEREWITH

An automatic patient recruitment system is operable generate abnormality data for medical scans by performing at least one inference function on image data of each medical scans by utilizing a computer vision model trained on a training set of medical scans. A subset of a plurality of patients is identified to be eligible for a pharmaceutical study by identifying medical scans having abnormality data that compares favorably to abnormality criteria of the pharmaceutical study. A size of the subset is compared to a minimum participant count requirement. A notification indicating the subset of the plurality of patients is transmitted based on the size of the subset comparing favorably to the minimum participant count requirement. 1. An automatic patient recruitment system , comprising:at least one processor; anda memory that stores operational instructions that, when executed by the at least one processor, cause the automatic patient recruitment system to:generate abnormality data for each of a plurality of medical scans of a plurality of patients via artificial intelligence by performing at least one inference function on image data of the plurality of medical scans, wherein the at least one inference function utilizes a computer vision model trained on a training set of medical scans;generate a plurality of subsets of the plurality of patients, wherein each subset of the plurality of patients corresponds to one of a plurality of pharmaceutical studies, wherein each subset of the plurality of patients is generated by identifying ones of the plurality of patients with corresponding ones of the plurality of medical scans having abnormality data that compares favorably to abnormality criteria of a set of eligibility criteria of the corresponding one of the plurality of pharmaceutical studies, and with corresponding patient data that compares favorably to other patient criteria of the set of eligibility criteria of the one of the plurality of pharmaceutical studies; ...

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

A control method for an image processing apparatus includes setting a virtual light source, analyzing characteristics of a partial region of a subject in an input image acquired through image capturing, smoothing at least a portion of the input image on the basis of information about a result of the analysis, generating, on the basis of the smoothed input image, reflected color components in a case where the subject is irradiated with light from the virtual light source, and performing correction based on the reflected color components on the input image. 1. An image processing apparatus comprising:at least one processor and/or circuit configured to function as following units, which area setting unit configured to set a virtual light source,an analysis unit configured to analyze characteristics of a partial region of a subject in an input image acquired through image capturing, anda processing unit configured to smooth at least a portion of the input image on the basis of information about an analysis result from the analysis unit, to generate, on the basis of the smoothed input image, reflected color components in a case where the subject is irradiated with light from the virtual light source, and to perform correction based on the reflected color components on the input image.2. The image processing apparatus according to claim 1 ,wherein the analysis unit analyzes characteristics of at least one of a three-dimensional shape, a frequency component, and color components of the partial region.3. The image processing apparatus according to claim 1 ,wherein the setting unit sets at least one of a position, an intensity, and a degree of light diffusion of the virtual light source.4. The image processing apparatus according to claim 3 ,wherein in a case where the degree of light diffusion of the virtual light source is high, the processing unit controls the reflected color components on the basis of an amount of change in a three-dimensional shape of the partial region ...

DETECTING AND MEASURING THE SIZE OF CLODS AND OTHER SOIL FEATURES FROM IMAGERY

The present disclosure provides systems and methods that measure soil roughness in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned clod detection model to determine a soil roughness value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field. 1. A computing system , comprising:one or more processors; and a machine-learned clod detection model configured to receive imagery and to process the imagery to detect soil clods depicted by the imagery; and', obtaining image data that depicts a portion of a field;', 'inputting the image data into the machine-learned clod detection model;', 'receiving one or more bounding shapes as an output of the machine-learned clod detection model, wherein each of the one or more bounding shapes indicates a respective location of a respective soil clod detected by the machine-learned clod detection model in the image data; and', 'determining a soil roughness value for at least the portion of the field based at least in part on the one or more bounding shapes., 'instructions that, when executed by the one or more processors, configure the computing system to perform operations, the operations comprising], 'one or more non-transitory computer-readable media that collectively store2. The computing system of claim 1 , wherein:the computing system is physically located on at least one of a work vehicle or an implement towed by the work vehicle;obtaining the image data of the field comprises obtaining image data captured by an imaging device that is physically located on at least one of the work vehicle or the implement towed by the work vehicle; andthe operations further ...

IMAGE PROCESSING APPARATUS, MEDICAL IMAGE DIAGNOSTIC APPARATUS, AND PROGRAM

According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns. 1. An image processing apparatus comprising:processing circuitry configured to:acquire medical image data;based on the medical image data, obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns; andcalculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns.2. The image processing apparatus according to claim 1 , wherein the processing circuitry claim 1 , by using the feature values in the predetermined region of the medical image claim 1 , identifies a kind of lesion claim 1 , body tissue claim 1 , or organ of the predetermined region.3. The image processing apparatus according to claim 1 , wherein the processing circuitry acquires the likelihood values according to a pixel value of a pixel in the medical image.4. The image processing apparatus according to claim 3 , wherein the processing circuitry acquires the likelihood values according to pixel values of a pixel and pixels that surround this pixel in the medical image.5. The image processing apparatus according to claim 4 , wherein:a plurality of distances between a pixel and pixels that surround this pixel are predetermined; ...

Method for Determining the Positions of a Plurality of Objects in a Digital Image

The present invention relates to a method and a system for determining the positions of a plurality of objects in a digital image by discriminating true positive positions of the plurality of objects from false positive candidate positions of the plurality of objects. In particular, the invention relates to a method for determining the positions of a plurality of objects in a digital image by discriminating true positive positions of the plurality of objects from false positive candidate positions of the plurality of objects, the plurality of objects being configured to receive molecules comprising genetic information. 1. A computer implemented method for determining positions of a plurality of objects in a digital image , the plurality of objects being configured to receive molecules comprising genetic information , the method comprising:determining a plurality of pixels in the digital image, each pixel having a local maximum intensity value with respect to intensity values of a plurality of neighboring pixels of the respective pixel;determining a variance of an intensity value of each pixel having a respective local maximum intensity value with respect to the intensity value of the respective pixel and the intensity value of each of the plurality of the respective neighboring pixels;weighting the intensity value of each of the plurality of pixels having a respective local maximum intensity value by the respective determined variance of the intensity value;creating a histogram comprising the respective weighted intensity of each pixel of the plurality of pixels each having a respective local maximum intensity value;determining a plurality of local minimums and Mild a global maximum of the histogram;selecting a local minimum of the plurality of local minimums as a threshold value for discriminating true positive positions of the plurality of objects from false positive positions of the plurality of objects, wherein the local minimum is selected under consideration ...

METHOD AND APPARATUS FOR OBTAINING SAMPLED POSITIONS OF TEXTURING OPERATIONS

Methods and apparatuses are disclosed for reporting texture footprint information. A texture footprint identifies the portion of a texture that will be utilized in rendering a pixel in a scene. The disclosed methods and apparatuses advantageously improve system efficiency in decoupled shading systems by first identifying which texels in a given texture map are needed for subsequently rendering a scene. Therefore, the number of texels that are generated and stored may be reduced to include the identified texels. Texels that are not identified need not be rendered and/or stored. 1. A method for obtaining a bitmap identifying texel locations corresponding to a pixel , comprising:obtaining a plurality of texture map coordinates in texture space;identifying a plurality of texel locations based on the plurality of texture map coordinates, wherein each texel location identifies a location of a texel in texture space that is covered by a projection of a pixel into the texture space;generating a bitmap representing the plurality of texel locations; andstoring the bitmap, wherein the bitmap is applicable for shading the pixel.2. The method of claim 1 , further comprising:obtaining gradient information for each of the plurality of texture map coordinates; andutilizing the gradient information in identifying the plurality of texel locations.3. The method of claim 1 , further comprising:obtaining a texture map level of detail parameter; andutilizing the texture map level of detail parameter in identifying the plurality of texel locations.4. The method of claim 1 , further comprising:obtaining a resolution specification indicating a number of texel locations to be represented for a dimension of the bitmap; andutilizing the resolution specification in generating the bitmap.5. The method of claim 1 , further comprising:generating a coarsening factor, wherein the coarsening factor indicates the number of texel locations represented per bit of the bitmap along a dimension of the ...

AUTOMATED BLOOD VESSEL FEATURE DETECTION AND QUANTIFICATION FOR RETINAL IMAGE GRADING AND DISEASE SCREENING

A method for vessel mapping and quantification. The method comprises pre-processing a retinal image to generate a vessel segmented image and processing the vessel segmented image to generate an image with a central light reflex. The method further includes identifying a cylindrical or tube-shaped region in the central light reflex and determining a closed contour representing the cylindrical shaped region and representing the closed contour by a function. The method computes a ratio of a minimum and maximum radius of the cylinder to determine an average shape of the cylinder associated with the central light reflex by using the function. The image is further processed to apply a morphological skeletonization operation to generate vessel centerlines and the segmented vascular network of the retinal image. In an example embodiment, a method for artery-vein nicking quantification for retinal blood vessels is performed by computing width of the vessel near and away from a cross over point of the vessels. In another example embodiment, a feature associate with the central light reflex is identified and compared with a second feature in the same location evaluated at a different time zone to confirm the associated shape of the light reflex. In another example embodiment, retinal focal arteriolar narrowing (FAN) is identified and quantified value is generated. In another example embodiment, a true optic disc is identified based on a combination of features and parameters associate with the vessel. 1. A method for vessel classification , the method comprising:pre-processing a retinal image to generate a vessel segmented image;first processing the vessel segmented image to generate an image with a central light reflex;identifying a cylindrical shaped region in the central light reflex;determining a closed contour representing the cylindrical shaped region and representing the closed contour by a function;computing a ratio of a minimum and maximum radius of the cylindrical ...

APPARATUS AND METHOD FOR IDENTIFYING CRACKS IN A STRUCTURE USING A MULTI-STAGE CLASSIFIER

A method of inspecting a structure for cracks is disclosed. The method includes the steps of moving an inspection vehicle along a pre-determined path through an area to be inspected, the inspection vehicle having a camera apparatus configured to scan the area to be inspected; using the camera apparatus to scan the area to be inspected at a pre-determined distance per second, thereby capturing scan data for analysis; and analyzing the scan data to classify cracks in the structure. 1. A method of inspecting a structure for cracks , comprising the steps of:moving an inspection vehicle along a pre-determined path through an area to be inspected, the inspection vehicle having a camera apparatus configured to scan the area to be inspected;using the camera apparatus to scan the area to be inspected at a pre-determined distance per second, thereby capturing scan data for analysis; andanalyzing the scan data to classify cracks in the structure.2. The method according to claim 1 , wherein the inspection vehicle is autonomous.3. The method according to claim 1 , wherein the inspection vehicle is remote controlled.4. The method according to claim 1 , wherein the step of analyzing includes the steps of:filtering the scan data to generate crack and scratch image frames;registering the crack and scratch image frames to generate a single mosaic image; andperforming shape and segmentation processing to remove small high amplitude disc noise, smooth fragmented cracks, and extract elongated targets that are crack like.5. The method according to claim 4 , wherein the step of filtering includes applying a three-stage filter to the scan image to remove non-uniform illumination claim 4 , separate the scan image into high amplitude scratch and low amplitude crack+grind frames claim 4 , and enhance high special frequency components of cracks.6. The method according to claim 4 , further including the step of generating texture and spatial frequency data for cracks.7. The method according to ...

Techniques for Controlled Generation of Training Data for Machine Learning Enabled Image Enhancement

Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model. 1. A method of training a machine learning model for enhancing images , the method comprising: accessing a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model;', 'accessing an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model; and', 'training the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model., 'using at least one computer hardware processor to perform2. The method of claim 1 , further comprising:capturing, using an imaging device, the target image of the displayed video frame using a first exposure time; andcapturing, using the imaging device, the input image of the displayed video frame using a second exposure time, wherein the second exposure time is less than the first exposure time.3. The method of claim 1 , further comprising:capturing, using an imaging device, the input image of the displayed video frame with a neutral density filter; andcapturing, using the imaging device, the target image of the displayed video frame without a neutral density filter.4. The method of claim 1 , further comprising:capturing, using an imaging device, the input image of the displayed video ...

MEANS FOR PRODUCING AND/OR CHECKING A PART MADE OF COMPOSITE MATERIALS

A method for producing and/or checking a part made of composite materials formed from one fabric having a surface whose texture exhibits a main orientation, including the following steps: obtaining a first image representing the texture of the fabric; determining an estimation relating to the main orientation of the texture, by determining for each pixel of the first image, an orientation of gradients relating to the luminance level of said pixel; determining an estimation of a global distribution of the orientations of gradients of the pixels of the first image; determining the main orientation as a function of the estimation of the global distribution of the orientations of gradients of the pixels of the first image; determining a deviation between the estimation relating to the main orientation and a setpoint value; and producing the part as a function of the deviation and/or emitting a check signal dependent on the deviation. 1. A method for producing and/or controlling a part made of composite materials formed from at least one fabric having a surface whose texture has a main orientation , wherein it includes the following steps of:obtaining a first image formed by a plurality of pixels and in which a luminance level can be determined for each pixel, representing the texture of the fabric;determining an estimate relating to the main orientation of the texture:during a first step, by determining, for each pixel of the first image, an orientation of gradients relating to the luminance level of said pixel;during a second step, by determining an estimate of an overall distribution of the gradient orientations of the pixels of the first image;during a third step, by determining the main orientation according to the estimate of the overall distribution of the gradient orientations of the pixels of the first image;determining a deviation between the estimate relating to the main orientation and a setpoint value;producing the part according to the deviation and/or ...

Prognosis of prostate cancer with computerized histomorphometric features of tumor morphology from routine hematoxylin and eosin slides

Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Method and device for filtering texture, using patch shift

Disclosed is a method and device for filtering texture, using a patch shift. A patch-based texture filtering method for removing texture from an image comprises the steps of: distinguishing a structure edge from patches for each pixel of an input image; and performing a patch shift for the patches of each pixel, on the basis of structure edge information of each of the patches to select a representative patch of each pixel. Accordingly, the method can generate a result image in which texture has been automatically and completely removed from a piece of the input image.

PEER-REVIEW FLAGGING SYSTEM AND METHODS FOR USE THEREWITH

A peer-review flagging system is operable to train a computer vision model and to generate automated assessment data by performing an inference function on a first medical scan by utilizing the computer vision model. Human assessment data is generated based on a first medical report written by a medical professional in conjunction with review of the first medical scan. First consensus data is generated based on the automated assessment data, the human assessment data, and a first threshold, and the first medical scan is determined to be flagged based on the first consensus data. A second threshold is selected use in generating second consensus data for a second medical scan and a second medical report written by the medical professional in conjunction with review of the second medical scan, and is selected to be stricter than the first threshold based on determining to flag the first medical scan. 1. A peer-review flagging system , comprising:at least one processor; and{'claim-text': ['train a computer vision model from a training set of medical scans by utilizing artificial intelligence;', 'generate first automated assessment data via artificial intelligence by performing a first inference function on a first medical scan by utilizing the computer vision model;', 'generate first human assessment data by performing an extraction function on a first medical report written by a first medical professional in conjunction with review of the first medical scan;', 'generate first consensus data by performing a consensus function on the first automated assessment data and the first human assessment data, wherein performing the consensus function includes calculating a first consensus score by comparing the first automated assessment data to the first human assessment data, and further includes comparing the first consensus score to a first threshold;', 'determine to flag the first medical scan for peer-review based on the first consensus data;', 'transmit a peer-review ...

AUTOMATIC NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

Provided herein are systems and computer-implemented methods for quantitative analyses of tissue sections (including, histopathology samples, such as immunohistochemically labeled or H&E stained tissue sections), involving automatic unsupervised segmentation of image(s) of the tissue section(s), measurement of multiple features for individual nuclei within the image(s), clustering of nuclei based on extracted features, and/or analysis of the spatial arrangement and organization of features in the image based on spatial statistics. Also provided are computer-readable media containing instructions to perform operations to carry out such methods. A quantitative image analysis pipeline for tumor purity estimation is also described 1. A system , comprising:an image-capture device configured to capture an image of a cell population, the image comprising input-pixel values of respective pixels of the image; and determine a feature image based at least in part on the input-pixel values, the feature image comprising per-pixel feature values associated with respective pixels of the pixels of the image;', 'determine a plurality of clusters based at least in part on the feature image, each cluster of the plurality of clusters associated with at least some of the pixels of the image;', 'select a first cluster of the plurality of clusters, the first cluster associated with nuclei of cells in the cell population;', 'determine a nuclei mask image representing pixels of the image associated with the first cluster; and', 'determine a plurality of per-nucleus mask images by applying morphological operations to the nuclei mask image., 'a control unit operatively connected with the image-capture device and configured to2. The system of claim 1 , wherein the image-capture device and/or the control unit is configured to carry out one or more operations automatically.3. The system of claim 1 , wherein the image of the cell population is a histopathology image.4. The system of claim 3 , ...

Detecting Fake Videos

In one embodiment, a method includes accessing a plurality of verified videos depicting one or more subjects, generating, based on the verified videos, a plurality of verified-video feature values corresponding to the subjects, generating, using one or more video transformations based on the verified videos, a plurality of fake videos, generating, based on the fake videos, a plurality of fake-video feature values, training a machine-learning model to determine whether a specified video is a genuine video, wherein the machine-learning model is trained based on the verified-video feature values and the fake-video feature values. The machine-learning model may be trained to classify the specified video in a genuine-video class or a fake-video class, and the machine-learning model maybe trained based on an association between a genuine-video class and the verified-video feature values and an association between a fake-video class and the fake-video feature values.

SYSTEM AND METHOD FOR SCAN-MATCHING ORIENTED VISUAL SLAM

A system and method for constructing and updating a two dimensional (2D) grid cell map based on depth images and RGB images. The method includes: providing an RGB image and a depth image by a sensor of a computing device; determining scores of pixels in the RGB image representing possibility of the pixels being located in a ground plane; determining normal directions of pixels in the depth image; determining the ground plane based on the scores and the normal directions; projecting the pixels in the depth images to the ground plane; and generating and updating the 2D grid cell map by defining the ground plane into grid cells and attributing a color descriptor to each grid cell. The color descriptor of each grid cell is calculated based on the projected pixels within that grid cell. 1. A method for generating and updating a 2D grid cell map , comprising:providing a red-green-blue (RGB) image and a depth image of a three-dimensional (3D) space by a sensor of a computing device, the sensor having extrinsic parameters;determining scores of pixels in the RGB image, wherein the scores representing possibility of the pixels in the RGB image being located in a ground plane;determining normal directions of pixels in the depth image;determining the ground plane based on the extrinsic parameters, the scores and the normal directions;projecting the pixels in the depth images to the ground plane to form projected pixels;when the 2D grid cell map is not available, generating the 2D grid cell map by defining the ground plane into grid cells and attributing a color descriptor to each grid cell, wherein the color descriptor of each grid cell is calculated based on the projected pixels within that grid cell; andwhen the 2D grid cell map is available, updating the 2D grid cell map by attributing a new color descriptor to each grid cell and updating that grid cell based on the new color descriptor.2. The method of claim 1 , wherein the sensor is an RGB-depth (RGBD) sensor and the ...

Identification or determination of a load based on texture

In one embodiment, the disclosure relates to a method for inspecting a load ( 101 ) in a container ( 100 ), comprising: classifying (S 2 ) one or more patches ( 11 ) of a digitized inspection image ( 10 ), the digitized inspection image ( 10 ) being generated by an inspection system ( 1 ) configured to inspect the container ( 100 ) by transmission of inspection radiation ( 3 ) from an inspection radiation source ( 31 ) to an inspection radiation detector ( 32 ) through the container ( 100 ), wherein the classifying (S 2 ) comprises: extracting (S 21 ) one or more texture descriptors (V, P) of a patch ( 11 ), and classifying (S 22 ) the patch ( 11 ), by comparing the one or more extracted texture descriptors (V, P) of the patch ( 11 ) to respective one or more reference texture descriptors (Vr, Wr, Pr) corresponding to respective one or more classes ( 202 ) of reference items ( 201 ), the one or more reference texture descriptors (Vr, Wr, Pr) of each class of reference items ( 201 ) being extracted from one or more reference images ( 20 ) of the one or more reference items ( 201 ).

CHEST RADIOGRAPH (CXR) IMAGE ANALYSIS

A method for estimating a presence of a pneumothorax abnormality. The method comprises classifying at least one texture feature of each of a plurality of pixels of a chest radiograph (CXR) image to generate an output map, identifying at least one lung contour in said CXR image, identifying a plurality of multiple pixel segments along said at least one lung contour, combining values of pixels in each one of said plurality of multiple pixel segments from said output map to generate a global descriptor for said CXR image, and estimating a presence of said pneumothorax abnormality in said CXR image by applying a statistical classifier on said global descriptor. 1. A method for estimating a presence of a pneumothorax abnormality , comprising:classifying at least one texture feature of each of a plurality of pixels of a chest radiograph (CXR) image to generate an output map;identifying at least one lung contour in said CXR image;identifying a plurality of multiple pixel segments along said at least one lung contour;combining values of pixels in each one of said plurality of multiple pixel segments from said output map to generate a global descriptor for said CXR image; andestimating a presence of said pneumothorax abnormality in said CXR image by applying a statistical classifier on said global descriptor.2. The method of claim 1 , wherein said classifying comprises:calculating at least one value of said at least one texture feature for each one of said plurality of pixels;calculating a plurality of feature descriptors each for another of said at least some pixels and based on respective said at least one value;compiling said output map mapping each one of said plurality of feature descriptors according to a location of a respective pixel of said plurality of pixels in said CXR image.3. The method of claim 2 , wherein said classifying comprises applying another statistical classifier on said at least one value to determine a respective said feature descriptor.4. The ...

MULTI-MODEL MEDICAL SCAN ANALYSIS SYSTEM AND METHODS FOR USE THEREWITH

A multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models are generated by performing a fine-tuning step on the generic model. Abnormality detection data is generated for a new medical scan by utilizing the generic model. A first one of the plurality of abnormality types that is detected in the new medical scan is determined based on a corresponding one of the plurality of probability values. Additional abnormality data is generated by performing a fine-tuned inference function on the image data of the new medical scan that utilizes one of the plurality of fine-tuned models that corresponds to the first one of the plurality of abnormality types. The additional abnormality data is transmitted for display.

MEDICAL PICTURE ARCHIVE INTEGRATION SYSTEM AND METHODS FOR USE THEREWITH

A medical picture archive integration system includes a de-identification system that includes a first memory designated for protected health information (PHI), operable to perform a de-identification function is on a DICOM image, received from a medical picture archive system, to identify at least one patient identifier and generate a de-identified medical scan that does not include the at least one patient identifier. The medical picture archive integration system further includes a de-identified image storage system that stores the de-identified medical scan in a second memory that is separate from the first memory, and an annotating system, operable to utilize model parameters received from a central server to perform an inference function on the de-identified medical scan, retrieved from the second memory to generate annotation data for transmission to the medical picture archive system as an annotated DICOM file. 1. A medical picture archive integration system , comprising:a receiver operable to receive data from a medical picture archive system;a transmitter operable to transmit data to the medical picture archive system;a network interface operable to bidirectionally communicate with a central server via a network, wherein the network does not include the medical picture archive system; receive, from the medical picture archive system via the receiver, a first medical image, wherein the first medical image includes at least one patient identifier; and', 'perform a de-identification function on the first medical image to identify the at least one patient identifier and generate a first de-identified medical scan that does not include the at least one patient identifier;, 'a de-identification system that includes a first memory designated for protected health information (PHI), operable to receive the first de-identified medical scan from the de-identification system; and', 'store the first de-identified medical scan in the second memory; and, 'a de-identified ...

TECHNIQUES FOR CONTROLLED GENERATION OF TRAINING DATA FOR MACHINE LEARNING ENABLED IMAGE ENHANCEMENT

Described herein are systems and techniques for generating training data for use in training a machine learning model for image enhancement. The system may access a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model. The system may access an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model. The system may train the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model. 1. A method of training a machine learning model for enhancing images , the method comprising:using at least one computer hardware processor to perform:accessing a target image of a displayed video frame, wherein the target image represents a target output of the machine learning model;accessing an input image of the displayed video frame, wherein the input image corresponds to the target image and represents an input to the machine learning model; andtraining the machine learning model using the target image and the input image corresponding to the target image to obtain a trained machine learning model.2. The method of claim 1 , further comprising:capturing, using an imaging device, the target image of the displayed video frame using a first exposure time; andcapturing, using the imaging device, the input image of the displayed video frame using a second exposure time, wherein the second exposure time is less than the first exposure time.3. The method of claim 1 , further comprising:capturing, using an imaging device, the input image of the displayed video frame with a neutral density filter; andcapturing, using the imaging device, the target image of the displayed video frame without a neutral density filter.4. The method of claim 1 , further comprising:capturing, using an imaging device, the input image of the displayed video frame; ...

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

An image processing apparatus and a method controlling the same extract an object included in image data and extract a highlight region of the object. A region including the highlight region and the pixels around the highlight region are obtained in the object. The lightness histogram of the pixels included in the region is generated, and the lightness of the pixels except the highlight region is adjusted based on the generated histogram. 1. An image processing apparatus comprising:a memory storing a set of instructions; andone or more processors that execute instructions of the set of the instructions and cause the image processing apparatus to function as:a first extraction unit configured to extract an object included in image data;a second extraction unit configured to extract a highlight region of the object extracted by the first extraction unit;a generation unit configured to generate a lightness histogram of pixels in a region including the highlight region extracted by the second extraction unit and a region around the highlight region in the object; andan adjustment unit configured to adjust the lightness of the pixels except the highlight region based on the lightness histogram generated by the generation unit.2. The apparatus according to claim 1 , wherein the generation unit obtains claim 1 , in the object claim 1 , a region including the highlight region and including pixels around the highlight region in an object obtained by excluding claim 1 , from a histogram target claim 1 , pixels of lightness greatly deviated from a mode of the lightness histogram of the object claim 1 , and generates a lightness histogram of pixels included in the region.3. The apparatus according to claim 1 , wherein a size of the region is a size at least 100 times a size of the highlight region.4. The apparatus according to claim 1 , whereinthe adjustment unit adjusts the lightness of the pixels using a lookup table, andthe lookup table has characteristics in which the ...

EDUCATION ASSISTING ROBOT AND CONTROL METHOD THEREOF

Disclosed are an education assisting robot and a control method thereof. The method includes: capturing and recognizing students' faces from shot images, and checking students' attendance; and capturing a teacher's face from the shot images and identifying a target, and target-following the teacher. The role of a target character can be automatically distinguished from the images, and different actions can be made for different target characters, including attendance checking and target following, so as to provide more different response functions and reduce the workload of teachers. 1. A control method of an education assisting robot , comprising:capturing and recognizing students' faces from shot images, and checking students' attendance; andcapturing a teacher's face from the shot images and identifying a target, and target-following the teacher.2. The control method of claim 1 , comprising:receiving input student photos to create a student sign-in form;shooting real-time images by a binocular camera;capturing and recognizing students' faces from the shot images by a deep face recognition algorithm; andmatching the recognized students' faces with the student photos of the student sign-in form to complete the attendance.3. The control method of claim 2 , comprising:texturing the images by using an LBP histogram and extracting face features;performing SVR processing on the face features to obtain 2D-aligned 2D faces;Deloni triangulating the faces based on key points of the 2D faces and adding triangles to edges of face contours;converting the triangulated faces to 3D faces facing forward; andobtaining student face recognition results by face representation, normalization and classification of the 3D faces.4. The control method of claim 1 , comprising:constructing a 2D map;capturing the teacher's face from the images shot by the binocular camera by a deep face recognition algorithm, and identifying the target;inferring, according to the images continuously shot by ...

SYSTEM AND METHOD FOR QUANTIFYING NOZZLE OCCLUSION IN 3D PRINTING

One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer. 1. A computer-executed method for determining a maintenance schedule for a three-dimensional (3D) printer nozzle , the method comprising:capturing an image of an orifice of the 3D printer nozzle while the 3D printer nozzle is jetting liquid droplets;analyzing the captured image to determine an occlusion fraction of the orifice of the 3D printer nozzle; anddetermining the maintenance schedule for the 3D printer nozzle based on the occlusion fraction and a predetermined occlusion threshold.2. The computer-executed method of claim 1 , further comprising adjusting the predetermined occlusion threshold based a quality requirement associated with printed products of the 3D printer.3. The computer-executed method of claim 1 , further comprising:capturing a sequence of images of the orifice over a time period;analyzing the sequence of images to determine time-varying changes of one or more occluded regions within the orifice.4. The computer-executed method of claim 1 , wherein analyzing the captured image comprises computing a gray-level co-occurrence matrix (GLCM) for the captured image to obtain a texture-analysis outcome associated with the captured image.5. The computer-executed method of claim 4 , further comprising:applying an adaptive thresholding technique on the texture-analysis outcome to obtain a ...

MEDICAL SCAN LABELING QUALITY ASSURANCE SYSTEM AND METHODS FOR USE THEREWITH

A medical scan system is operable to receive a set of labeling data corresponding to a set of medical scans from each of a set of client devices corresponding to a set of users. The set of medical scans and each set of labeling data is transmitted to an expert client device associated with an expert user, and a set of golden labeling data and a plurality of sets of correction data are received from the expert client device. A set of performance score data is generated based on the plurality of sets of correction data, and each performance score data of the set of performance score data is assigned to a corresponding one of the set of users. An updated training set that includes the set of golden labeling data is generated, and a medical scan analysis function is retrained based on the updated training set. 1. A medical scan system , comprising:at least one processing system that includes a processor; and{'claim-text': ['train a medical scan analysis function based on a training set, the medical scan function utilizing artificial intelligence;', 'transmit a set of medical scans to a set of client devices associated with a set of users;', 'receive a set of labeling data from each of the set of client devices, wherein each set of labeling data is generated by a corresponding one of the set of client devices, wherein each set of labeling data includes labeling data for each of the set of medical scans, and wherein the labeling data for each of the set of medical scans is generated by the corresponding one of the set of client devices in response to at least one prompt to provide the labeling data via a first interactive interface in conjunction with display of the each of the set of medicals scans;', 'transmit the set of medical scans and each set of labeling data to an expert client device associated with an expert user that is not included in the set of users;', 'receive a set of golden labeling data and a plurality of sets of correction data from the expert client ...

Systems and Methods for Hybrid Depth Regularization

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map. 1. A depth sensing system , comprising:a plurality of cameras;a processor;a memory containing an image processing application; obtain image data for a plurality of images from multiple viewpoints using the plurality of cameras, wherein the image data for the plurality of images comprises a reference image and at least one alternate view image;', 'generate a raw depth map containing depth estimates for pixels within the reference image using the image data for the reference image and the image data for the at least one alternate view image using a first depth estimation process, and a confidence map describing reliability of depth estimates contained within the raw depth map; and', computing a secondary depth map containing depth estimates for pixels within the reference image using a second different depth estimation process; and', 'computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map, where a depth estimate for a pixel in the reference image is selected from the raw depth map when the depth estimate is indicated as being ...

Method for controlling x-ray image diagnosis apparatus and x-ray generation device

To provide a method for controlling an X-ray image diagnostic apparatus and an X-ray generation device equipped with an ABS system tracking movement of an object position without an operator performing setting operation for a region of interest (ROI), the determination condition storage unit ( 6 i ) storing ROI position determination conditions in which image statistical information is used for defining conditions to determine a position of an ROI in an X-ray image out of plural blocks generated by dividing the X-ray image into plural regions, the first block statistical information calculation unit ( 6 f ) calculating the image statistical information for each of plural blocks, and the region-of-interest position selection unit ( 6 g ) selecting a block that serves as an ROI from among plural blocks using the ROI position determination conditions and the image statistical information of each block are provided. A feedback value to be used in controlling the brightness value of the ROI is calculated based on the brightness value of the ROI, and irradiation conditions are determined so that the feedback value reaches a target brightness value.

MEDICAL SCAN TRIAGING SYSTEM AND METHODS FOR USE THEREWITH

A medical scan triaging system is operable to train a computer vision model and to generate abnormality data indicating abnormality probabilities for medical scans via the computer vision model. A first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a first probability value of a triage probability threshold. A second subset of medical scans is determined by identifying medical scans with abnormality probabilities less than the first probability value. An updated first subset of medical scans is determined by identifying medical scans with abnormality probabilities greater than a second probability value of an updated triage probability threshold. An updated second subset of the plurality of medical scans is determined by identifying medical scans with a abnormality probabilities less than the second probability value. The updated first subset of medical scans is transmitted to client devices. 1. A method for execution by a medical scan triaging system , the method comprising:training a computer vision model by performing a training step upon image data of a training set of medical scans, the computer vision model utilizing artificial intelligence;generating abnormality data for each of a plurality of medical scans by utilizing the computer vision model to perform an inference function on image data of each of the plurality of medical scans via the artificial intelligence, wherein the abnormality data for the each of the plurality of medical scans indicates an abnormality probability indicating a probability that an abnormality is present in the each of the plurality of medical scans;determining a first subset of the plurality of medical scans designated for human review by identifying ones of the plurality of medical scans with a corresponding abnormality probability greater than a first probability value indicated by a triage probability threshold;determining a second subset of the plurality of ...

BREAST IMAGING REPORTING AND DATA SYSTEM (BI-RADS) TISSUE COMPOSITION

Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms. 1. A method of assessing breast density for breast cancer risk applications , the method comprising:receiving raw digital image data including a plurality of pixels;performing a statistical analysis with optimization on the raw digital image data by determining a measured variation of the raw digital image data based on a deviation of the pixel values within a breast region;{'sub': 'pg', 'determining four-state ordinal variables (BR) from the measured variation; and'}{'sub': 'pg', 'associating the four-state ordinal variables (BR) with a measure of risk for breast cancer.'}2. The method of claim 1 , further comprising approximating a portion of a breast represented within the raw digital image data that was in contact with a compression paddle during image acquisition.3. The method of claim 1 , wherein determining the four-state ordinal variables comprises applying differential evolution optimization.4. The method of claim 3 , wherein applying the differential evolution optimization comprises maximizing or minimizing a fitness function by a repeated processing of image case-control datasets with plural random vectors for a given ...

COMPUTER VISION MODEL TRAINING VIA INTENSITY TRANSFORM AUGMENTATION

An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device. 1. An intensity transform augmentation system , comprising:at least one processor; and generate random distribution parameters for a training set of medical scans;', 'generate a plurality of augmented images, wherein each of the plurality of augmented images is generated by performing an intensity transformation function on one of the training set of medical scans utilizing the random distribution parameters, wherein performing the intensity transformation function on the one of the training set of medical scans includes calculating each one of a final plurality of greyscale pixel values of the augmented image as a deterministic function of each corresponding one of an initial plurality of greyscale pixel values of image data of the one of the training set of medical scans;', 'generate a computer vision model by performing a training step on the plurality of augmented images;', 'receive, via the receiver, a new medical scan;', 'generate inference data by performing an inference function that utilizes the computer vision model on the new medical scan; and', 'transmit, via a ...

Method and apparatus for providing augmented reality

A method and apparatus for providing augmented reality are provided and include a controller that is configured to match a head up display area of a windshield and an input image and determine an area in which an information amount is a minimum in the matched image as a position of a display window to display virtual information. In addition, the controller is configured to output virtual information at the determined position.

IMAGE FORMING APPARATUS

An image forming apparatus includes an image forming device to form a correction image, an image density detector to detect image densities of a plurality of areas in the correction image, and circuitry to correct an image formation condition of the image forming device based on detected image densities of the plurality of areas. The circuitry replaces a detected image density of an area of interest selected from the plurality of areas with an average value of detected image densities of two or more areas including adjacent areas adjacent to the area of interest and corrects the image formation condition of the image forming device based on the detected image densities of the plurality of areas after replacement when a difference between the detected image density of the area of interest and at least one of the detected image densities of the adjacent areas exceeds a predetermined threshold. 1. An image forming apparatus comprising:an image forming device configured to form a correction image;an image density detector configured to detect image densities of a plurality of areas in the correction image; andcircuitry configured to correct an image formation condition of the image forming device based on detected image densities of the plurality of areas detected by the image density detector, execute replacement processing to replace a detected image density of an area of interest selected from the plurality of areas with an average value of detected image densities of two or more areas including adjacent areas adjacent to the area of interest; and', 'correct the image formation condition of the image forming device based on the detected image densities of the plurality of areas after the replacement processing,', 'when a difference between the detected image density of the area of interest and at least one of the detected image densities of the adjacent areas exceeds a predetermined threshold., 'the circuitry configured to2. The image forming apparatus according to ...

Medical evaluation system and method for use therewith

A medical evaluation system operates by: receiving a set of medical scans of a medical scan protocol captured for a patient, the set of medical scans corresponding to a proper subset of a plurality of sequence types; generating abnormality data by performing an inference function on the set of medical scans, wherein the inference function utilizes a computer vision model trained on a plurality of medical scans corresponding to the proper subset of the plurality of sequence types; calculating a confidence score for the abnormality data; generating first additional sequence data, wherein when the confidence score compares unfavorably to a confidence score threshold, the first additional sequence data indicates at least one first additional medical scan of the patient, corresponding to a first at least one of the plurality of sequence types not included in the proper subset of the plurality of sequence types, and when the confidence score compares favorably to the confidence score threshold, the first additional sequence data indicates no further medical scans of the patient; and transmitting the first additional sequence data.

Heat map generating system and methods for use therewith

A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

Salt and pepper noise filtering method and device based on morphological component analysis

The present application provides a salt and pepper noise filtering method and device based on morphological component analysis. The method comprises: obtaining a to-be-filtered image containing salt and pepper noise; calculating the dimension of the to-be-filtered image, labeled as [n, m]; initializing an n*m-dimensional all-1 labeled matrix as a salt and pepper noise labeled map; obtaining a preset region centered on a pixel point with a pixel value of 0 or 255, and calculating a noise variance between the pixel points in the preset region; labeling the position of a salt and pepper noise point in the salt and pepper noise labeled map according to the noise variance between the pixel points in the preset region, and updating and determining the salt and pepper noise labeled map. The salt and pepper noise is filtered through the method based on morphological component analysis, which improves the quality of the image.

Information processing system and information processing method

An information processing system comprises: a light emitting unit that irradiates pattern light having a specific pattern to a subject to form a texture on the subject; an imaging unit that captures the subject on which the texture is formed; a deriving unit that derives distance information to the subject based on an image captured by the imaging unit; an analyzing unit that analyzes whether or not an image abnormality is present in either one of the captured image and an image based on the distance information; and a dimming unit that performs dimming control when the analyzing unit analyzes that the image abnormality is present.

Histogram and entropy-based texture detection

A mechanism is described for facilitating histogram and entropy-based texture detection in computing systems, according to one embodiment. A method of embodiments, as described herein, includes detecting an image of a scene, and computing multi-scale local binary pattern (LBP) image for the detected image based on a noise model associated with the detected image. The method may further include calculating a local histogram based the multi-scale LBP image, and calculating LBP entropy based on the local histogram. The method may further include applying a threshold to convert the LBP entropy into textureness of a texture map of the detected image for processing by an image signal processing (ISP) engine.

IMAGE PROCESSING APPARATUS, MEDICAL IMAGE DIAGNOSTIC APPARATUS, AND PROGRAM

According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns. 1. An image processing apparatus comprising processing circuitry configured to:acquire medical image data;based on a pixel value of the medical image data, obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern included in the medical image data based on each of a plurality of textual patterns;calculate a feature value based on a likelihood value of each of the plurality of textual patterns; andidentify a kind of lesion, body tissue, or organ included in the medical image based on the feature value.2. The image processing apparatus according to claim 1 , wherein the processing circuitry acquires the likelihood values according to pixel values of each pixel and pixels that surround the each pixel.3. The image processing apparatus according to claim 2 , wherein the processing circuitry sets a plurality of distances between the each pixel and the pixels that surround the each pixel claim 2 , and acquires the likelihood values according to the pixel values of the each pixel and the pixels that surround the each pixel for each of the distances. This application is a continuation of U.S. Application Ser. No. 16/023,864, filed Jun. 29, 2018, which is based upon and claims the benefit of priority from the prior Japanese Patent Application No. 2017-129423, filed Jun. 30, 2017, the ...

OPHTHALMOLOGIC IMAGE PROCESSING METHOD AND FUNDUS IMAGING APPARATUS

An image processor performs a histogram acquisition step of acquiring a histogram representing a distribution of gradation values of pixels in a fundus color image captured by irradiating a fundus with a plurality of beams of single-color light having different wavelengths, the histogram being acquired for each channel corresponding to each beam of single-color light, a histogram correction step of acquiring a corrected histogram by correcting the histogram of each channel acquired in the histogram acquisition step, of which a target pattern is set for each channel in advance, so as to fit to the corresponding target pattern, and a color tone corrected image generation step of generating a color tone corrected image, in which a distribution of gradation values for each channel is represented by the corrected histogram, based on the corrected histogram of each channel. 1. An ophthalmologic image processing method comprising:an imaging step of irradiating a fundus with a plurality of beams of single-color light having different wavelengths to capture a fundus color image based on fundus reflection light of the plurality of beams of single-color light, the imaging steps is performed in a fundus imaging apparatus; anda color tone corrected image generation step of generating a color tone corrected image with using a computer by correcting a gradation value of each pixel in the fundus color image such that a feature value for each channel corresponding to the beam of single-color light, which is based on a distribution of gradation values of pixels in the fundus color image, fits to a feature value in a target pattern predetermined for each channel.2. The ophthalmologic image processing method according to claim 1 ,wherein the feature value is a value defining a histogram based on the distribution of gradation value of each pixel.3. The ophthalmologic image processing method according to claim 2 ,wherein the histogram has a population of pixels configuring a partial area ...

Image processing apparatus and method

An image analysis unit of an image processing apparatus acquires a distribution condition of feature points in an entire input image and in each of a plurality of small regions in the input image. A target point number setting unit sets a target point number for each small region based on the distribution condition of feature points in the entire input image. An expected point number setting unit sets an expected point number for each small region based on the distribution condition of feature points in the small region. A comparison unit compares the target point number and the expected point number. A sensitivity setting unit sets detection sensitivity based on the comparison result. A feature point detection unit performs feature point detection according to the detection sensitivity.

HISTOGRAM-BASED EDGE/TEXT DETECTION

Described is a system and method for dynamically changing encode quality at a block level based on runtime pre-encoding analysis of content in a video stream. A video encoder continuously analyzes the content during runtime, and collects statistics and/or characteristics of the content before encoding it. This classifies the block among pre-defined categories of content, where every category has its own compression parameters. 1. A method for dynamically encoding an image , the method comprising:receiving a frame of a video stream;dividing the frame into a plurality of blocks;forming a respective histogram for each of the plurality of blocks, wherein each histogram includes a plurality of bins indicating a count of pixel intensities; anddetermining a respective content type for the respective histogram of each of the plurality of blocks.2. The method of claim 1 , further comprising:selecting encoding parameters for each of the plurality of blocks based the respect content type ; andencoding the frame based on the encoding parameters selected.3. The method of claim 2 , wherein the respective content type is determined by taking a discrete time derivative of the plurality of bins in a respective histogram.4. The method of claim 3 , wherein the respective content type is determined by comparing a summation of the discrete time derivative of the plurality of bins in respective histogram to a first threshold.5. The method of claim 4 , wherein the respective content type is determined to be text with the summation exceeds the first threshold and is less than a second threshold.6. The method of claim 5 , wherein the encoding parameter selected for text includes a Quantization Parameters (QP) that is lower than for other content types.7. The method of claim 1 , wherein the respective content type is determined by comparing the respective histogram to a database of histograms.8. The method of claim 7 , wherein the comparing is performed by performing a Fast Fourier ...

SYSTEM AND METHOD FOR DETERMINING 3D SURFACE FEATURES AND IRREGULARITIES ON AN OBJECT

This invention provides a system and method for determining the location and characteristics of certain surface features that comprises elevated or depressed regions with respect to a smooth surrounding surface on an object. A filter acts on a range image of the scene. A filter defines an annulus or other perimeter shape around each pixel in which a best-fit surface is established. A normal to the pixel allows derivation of local displacement height. The displacement height is used to establish a height deviation image of the object, with which bumps, dents or other height-displacement features can be determined. The bump filter can be used to locate regions on a surface with minimal irregularities by mapping such irregularities to a grid and then thresholding the grid to generate a cost function. Regions with a minimal cost are acceptable candidates for application of labels and other items in which a smooth surface is desirable. 1. A system for determining surface height variations on an object based upon a range image comprising.a camera assembly that generates a range image;a vision system that analyzes the range image;a filter that (a) generates a best-fit surface about each of selected pixels in the range image, (b) establishes a normal to the best-fit surface, respectively for each of the pixels, and (c) determines a relative height displacement of a region about each of the pixels relative to the best-fit surface; anda vision system tool that analyzes height displaced features localized by the filter.2. The system as set forth in wherein the filter includes a background region about each of the selected pixels from which the best-fit surface is established.3. The system as set forth in wherein the filter includes a foreground region claim 2 , located proximate to the background region and including at least one of the selected pixels in which the relative height displacement is determined.4. The system as set forth in wherein at least one of a size and a ...

MEDICAL SCAN HEADER STANDARDIZATION SYSTEM AND METHODS FOR USE THEREWITH

A medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model. 1. A medical scan header standardization system , comprising:at least one processor; and receive a plurality of DICOM images;', 'determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of the plurality of DICOM images;', 'determine a standard set of Digital Imaging and Communications in Medicine (DICOM) header entries for at least one of the standard set of fields by including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold;', 'receive a DICOM image;', 'determine to correct an entry of a field the header of the DICOM image;', 'select one of the standard set of DICOM header entries for the field to replace the entry of the field of the header of the DICOM image;', 'train a computer vision model via artificial intelligence utilizing a training set of DICOM images that includes the DICOM image and the one of the standard set of DICOM header entries; and', 'generate inference data for at least one new medical scan based on performing an inference function on image data of the at least one ...

TARGET TRACKING METHOD AND SYSTEM ADAPTABLE TO MULTI-TARGET TRACKING

A target tracking method and system adaptable to multi-target tracking include performing global-search detection on a current image to obtain candidates of the current image. Association between the candidates and a tracked target is performed to determine similarity between the candidates and the tracked target and to give corresponding similarity values to the candidates. The candidate with a maximum similarity value is defined as an associated candidate of the tracked target. Candidates with non-zero similarity values and primary-object classification other than the associated candidate are filtered out and defined as filtered candidates. New tracked targets are generated according to the associated candidate and the filtered candidates. 1. A target tracking method adaptable to multi-target tracking , comprising:performing global-search detection on a current image to obtain candidates of the current image;performing association between the candidates and a tracked target to determine similarity between the candidates and the tracked target and to give corresponding similarity values to the candidates;selecting a candidate with a maximum similarity value, the selected candidate being defined as an associated candidate of the tracked target;filtering out candidates with non-zero similarity values and primary-object classification other than the associated candidate, resulting in filtered candidates; andgenerating new tracked targets according to the associated candidate and the filtered candidates.2. The method of claim 1 , wherein if a previous image has no tracked target claim 1 , the following steps are performed:determining whether classification of the candidate of the current image is a primary object; anddefining the candidate with primary-object classification as a new tracked target if the classification is the primary object.3. The method of claim 1 , wherein if no associated candidate exists claim 1 , the following steps are performed:projecting a ...

MAMMOGRAPHY APPARATUS

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant 1. A method of processing an X-ray image of a person's breast to determine presence of a malignancy , the method comprising:locating a region of interest (CE-ROI) in an X-ray image of a person's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image;determining a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel;using a convolutional neural network (CNN) to process indications of intensity provided by a plurality of X-ray pixels that are located within the texture neighborhood to determine features of the X-ray image for use as components of a texture feature vector (TF) for the texture neighborhood;generating a texture feature vector comprising the determined features as components; andusing a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.2. The method according to and comprising ...

Augmented X-Ray Imaging System For Detection of Threats

A pre-trained convolutional neural network is trained to accurately identify threats in x-ray images of luggage. The x-ray images are filtered according to a threshold to separate out the outlines of dense objects prior to the analysis by the trained convolutional neural network. 1. A system for identifying weapons in luggage , comprising:an x-ray imaging system for producing x-ray images of luggage;a processor configured to apply a threshold to the x-ray images to separate out outlines of dense objects that pass the threshold; anda convolutional neural network for analyzing the outlines of the dense objects to identify weapons in the x-ray images.2. The system of claim 1 , wherein the convolutional neural network is a pre-trained convolutional neural network.3. The system of claim 2 , wherein the pre-trained convolutional neural network is further trained on a database of images of knives and handguns.4. The system of claim 3 , wherein the database of images of knives and handguns include partial images of the knives and handguns that do not include a complete outline of the knives and handguns.5. The system of claim 1 , wherein the x-ray imaging system is a dual-beam x-ray imaging system.6. The system of claim 1 , wherein the processor is further configured to apply a minimum size requirement to the separated items.7. The system of claim 6 , wherein the processor is further configured to apply a smoothing filter to the outlines of the separated items.8. The system of claim 7 , wherein the smoothing filter is a Gaussian filter.9. The system of claim 1 , wherein the convolutional neural network further comprises a graphics processing unit.10. A method claim 1 , comprising:obtaining a pre-trained convolutional neural network that is pre-trained on an image database that does not include images of knives and handguns;training the pre-trained convolutional neural network on a database of knife and handgun images to provide a trained convolutional neural network that ...

IMAGE SCANNER, METHOD, AND COMPUTER-READABLE MEDIUM FOR DETECTING DOCUMENT EDGE POSITIONS

An image scanner includes a document detector configured to, each time line scanning is repeated a predetermined number of times, perform an edge detecting process to detect a position of a lateral side extending in a sub scanning direction from a non-reference corner of a sheet, in the edge detecting process performed for a first time, set a position of the non-reference corner in a main scanning direction as a detection reference position, in the edge detecting process performed for a second or later time, set a previously-detected position of the lateral side in the main scanning direction as the detection reference position, and in each edge detecting process, set a detection range in the main scanning direction on the basis of the detection reference position, and detect a specific pixel corresponding to the lateral side in the main scanning direction within the detection range in the main scanning direction. 1. An image scanner comprising:a document table configured to support a rectangular sheet placed thereon, the document table having a specific position thereon where one of four corners of the sheet is placed as a reference corner;an image sensor configured to repeatedly perform line scanning to scan the sheet placed on the document table in a main scanning direction while moving in a sub scanning direction perpendicular to the main scanning direction, thereby generating image data representing a scanned image of the sheet; and detect a non-reference corner of the sheet placed on the document table, the non-reference corner being opposed to the reference corner across an upper side of the sheet in the main scanning direction, the upper side extending in the main scanning direction from the reference corner;', 'each time the image sensor repeatedly performs the line scanning a predetermined number of times while moving in the sub scanning direction, perform an edge detecting process to detect a position of a lateral side extending in the sub scanning ...

Systems and methods for real-time tracking of a target tissue using imaging before and during therapy delivery

Described herein are systems and methods for tracking a target tissue during therapy delivery. A system for identifying an anatomical structure and tracking the motion of the anatomical structure using imaging before and during delivery of a therapy to a patient includes an imaging module and a therapy module. In some cases, the imaging module is configured to identify a region of the anatomical structure in an image, and the therapy module is configured to deliver the therapy to a target tissue. A method for imaging during delivery of a therapy includes acquiring an image, identifying a region of an anatomical structure, tracking the region of the anatomical structure, integrating the tracking, generating a unique template library, determining if a pre-existing template matches the results or if the results should be updated as a new template, and delivering the therapy to the target tissue.

HYBRID MACHINE LEARNING SYSTEMS

A machine learning system for processing image data obtained from an image sensor is provided. The system includes a front end comprising one or more hard-coded filters, each of the one or more hard-coded filters being arranged to perform a set task. The system includes a neural network arranged to receive and process output from the front end. The one or more hard-coded filters include one or more hard-coded noise compensation filters that are hard-coded to compensate for a noise profile of the image sensor from which the image data is obtained. A method of processing image data in a machine learning system is also provided. A system for processing image data is provided. 1. A machine learning system for processing image data obtained from an image sensor , the system comprising:a front end comprising one or more hard-coded filters, each of the one or more hard-coded filters being arranged to perform a set task; anda neural network arranged to receive and process output from the front end,wherein the one or more hard-coded filters include one or more hard-coded noise compensation filters that are hard-coded to compensate for a noise profile of the image sensor from which the image data is obtained.2. The system according to claim 1 , wherein the front end comprises one or more segmentation operators.3. The system according to claim 1 , wherein the front end comprises one or more color feature extractors.4. The system according to claim 3 , the system being configured to provide input pixels obtained from the image sensor to the one or more color feature extractors via one or more input pixel streams.5. The system according to claim 4 , the one or more color feature extractors being configured to convert the input pixels in the one or more input pixel streams into YUV color space components without clipping.6. The system according to claim 4 , the one or more color feature extractors being configured to extract hue and saturation components from the one or more ...

DISPLAY CONTROL METHOD, DISPLAY CONTROL DEVICE AND COMPUTER-READABLE STORAGE MEDIUM

A display control method includes: image texture data of a first display area are extracted; a pixel mapping is performed on the image texture data to enable the first display area and the second display area to obtain consistent visual display effects; and the image texture data of the first display area are updated to the pixel-mapped image texture data for displaying. 1. A display control method , applied to a terminal comprising a first display area and a second display area with different pixel densities , the display control method comprising:extracting image texture data of the first display area;performing a pixel mapping on the image texture data to enable the first display area and the second display area to obtain consistent visual display effects; andupdating the image texture data of the first display area to the pixel-mapped image texture data for displaying.2. The display control method of claim 1 , wherein the performing a pixel mapping on the image texture data to enable the first display area and the second display area to obtain consistent visual display effects comprises:mapping a pixel value of a pixel point corresponding to the image texture data in the first display area to an average value of pixel values of a set number of pixel points in the second display area.3. The display control method of claim 2 , wherein the set number of pixel points are obtained according to at least one of a pixel distribution of the first display area or a pixel distribution of the second display area.4. The display control method of claim 1 , wherein a pixel density of the first display area is lower than a pixel density of the second display area.5. The display control method of claim 4 , wherein the first display area is an area of setting a camera under panel (CUP); andthe second display area is a display area adjacent to the area of setting the CUP.6. The display control method of claim 1 , wherein the extracting image texture data of the first display area ...

Extraction of a bias field invariant biomarker from an image

The present invention provides a method of computer analysis of a data set representing an image to extract a texture based measure therefrom, said image including a multiplicative bias in intensity within the image of unknown magnitude, the method comprising applying to said data set a bank of texture extracting filters, such that said filters are chosen from filters that are invariant to the presence of a multiplicative bias field. By providing such a method, rather than attempting to correct the bias field before extraction of a texture based-biomarker, a texture-based biomarker that is bias field invariant is extracted. This makes correction of the bias field unnecessary. 1. A method of computer analysis of a data set representing an image to extract a texture based measure therefrom , said image including a multiplicative bias in intensity within the image of unknown magnitude , the method comprising applying to said data set a bank of texture extracting filters , such that said filters are chosen from filters that are invariant to the presence of a multiplicative bias field.3. A method as claimed in claim 2 , wherein each of the three eigenvalues of the Hessian matrix is applied as a texture extracting filter.5. A method as claimed in any preceding claim claim 2 , wherein a texture extracting filter applied in said method is the Laplacian of the Gaussian defined by:{'br': None, 'sup': '2', 'i': G', 'x', 'x', 'x', 'x, 'sub': 1', '2', '3, '∇(; σ)=λ(; σ)+λ(; σ)λ(; σ);'}6. A method as claimed in any preceding claim claim 2 , wherein a texture extracting filter applied in said method is the Gaussian curvature defined by:{'br': None, 'i': K', 'x', 'x', 'x', 'x, 'sub': 1', '2', '3, '(; σ)→λ(; σ)λ(; σ)λ(; σ);'}7. A method as claimed in any preceding claim claim 2 , wherein a texture extracting filter applied in said method is the the Frobenius norm of the Hessian matrix defined by:{'br': None, 'i': H', 'x', 'x', 'x', 'x, 'sub': F', '1', '2', '3, 'sup': 2', '2', '2, ...

CONVOLUTIONAL NEURAL NETWORK AND ASSOCIATED METHOD FOR IDENTIFYING BASAL CELL CARCINOMA

A convolutional neural network (CNN) and associated method for identifying basal cell carcinoma are disclosed. The CNN comprises two convolution layers, two pooling layers and at least one fully-connected layer. The first convolution layer uses initial Gabor filters that model the kernel parameters setting in advance based on human professional knowledge. The method uses collagen fiber images for training images and converts doctors' knowledge to initiate the Gabor filters as featuring computerization. The invention provides better training performance in terms of training time consumption and training material overhead. 1. A convolutional neural network , comprising layers in an order of:an input component receiving a batch of an image;a first convolution layer with initial Gabor filters that generates a first feature map;a first pooling layer reducing dimensions of the first feature maps;a second convolution layer with random kernels that generates a second feature maps;a second pooling layer reducing dimensions of the second feature maps; anda first fully-connected layer classifying the second feature maps into an output component.2. The convolutional neural network as claimed in further comprising a third pooling layer defined between the input component and the first convolution layer.3. The convolutional neural network as claimed in claim 1 , wherein a number of kernels to the initial Gabor filters corresponded to a number of frequency carriers multiplied by a number of orientation vectors.4. The convolutional neural network as claimed in claim 3 , wherein the number of frequency carriers is determined based on the scale of targeting features and the pixel size of the image.5. The convolutional neural network as claimed in claim 4 , wherein each initial Gabor filter is defined into a Fourier transformation of Gabor-filter in an equation of:{'br': None, 'i': G', 'u,v;σ', 'e, 'sub': x', 'y, 'sup': −1/2((σ', {'sub2': 'x'}, '(u′−ω))', {'sup2': '2'}, '+(σ', {'sub2 ...

DETERMINING POSITIONS AND ORIENTATIONS OF OBJECTS

Methods and apparatus for determining poses of objects acquire plural images of the objects from different points of view. The images may be obtained by plural cameras arranged in a planar array. Each image may be processed to identify features such as contours of objects. The images may be projected onto different depth planes to yield depth plane images. The depth plane images for each depth plane may be compared to identify features lying in the depth plane. A pattern matching algorithm may be performed on the features lying in the depth plane to determine the poses of one or more of the objects. The described apparatus and methods may be applied in bin-picking and other applications. 146-. (canceled)47. Apparatus for determining object pose , the apparatus comprising:a plurality of cameras arranged to obtain a corresponding plurality of camera images of a field of view, the field of view comprising a plurality of different views of one or more objects contained within a target volume; anda data processor connected to receive and process the camera images to generate a plurality of depth plane images and to process the plurality of depth plane images to locate one or more object features wherein the plurality of depth plane images includes a set of depth plane images corresponding to each of a plurality of depth planes and the object features include object features in each of two or more of the depth planes and to determine a pose of one or more of the objects contained within the target volume based on the located object features.48. The apparatus according to wherein the processor is configured to:process the plurality of camera images to generate a first set of depth plane images corresponding to a first depth plane;process the first set of depth plane images to locate one or more object features in the first set of depth plane images; andtransform the first set of depth plane images to yield one or more sets of transformed depth plane images, each set of ...

Hyperspectral scanning to determine skin health

A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.

Method, Apparatus, Storage Medium and Device for Controlled Synthesis of Inhomogeneous Textures

The present invention provides a method, apparatus, storage medium and device for controlled synthesis of inhomogeneous textures, which relates to the field of texture synthesis technique in image processing. The method includes: obtaining an input texture exemplar from user, and analyzing the features of the exemplar, so as to extract the source guidance channels; herein the guidance channels comprise a progression map and an orientation field of the texture exemplar; obtaining a progression map and an orientation field from the user specified target, which are used as the target guidance channels; comparing the differences between the source guidance channels and the target guidance channels, so as to form the progression constraint and orientation constraint; determining the distance measure between the texture exemplar and target image, and adding the progression constraint and orientation constraint into the distance measure; the target image produced by minimizing the sum of distances over all patches with the constrained distance measure, is the final output of controlled synthesis. 1. A method for controlled synthesis of inhomogeneous textures , comprising:Obtaining an input texture exemplar from user, and analyzing the features of the exemplar, so as to extract the source guidance channels; the guidance channels comprise a progression map and an orientation field of the texture exemplar;Obtaining a progression map and an orientation field from the user specified target, which are used as the target guidance channels;Comparing the differences between the source guidance channels and the target guidance channels, so as to form the progression constraint and orientation constraint;Determining the distance measure between the texture exemplar and target image, and adding the progression constraint and orientation constraint into the distance measure;The target image produced by minimizing the sum of distances over all patches with the constrained distance ...

Mammography apparatus

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

METHOD FOR RESTORING VIDEO DATA OF DRAINAGE PIPE BASED ON COMPUTER VISION

A method for restoring video data of a pipe based on computer vision is provided, including: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm. 1 {'br': None, 'i': g', 'x,y', 'H', 'f', 'x,y, '()=[()]\u2003\u2003(1)'}, 'step (1): collecting image/video information of a pipe by a pipe robot with a high-definition camera entering the pipe, and performing gray stretching on the collected pipe image/video; enhancing contrast of the pipe image to make light and shade contrast of the pipe image more distinct and features more obvious; wherein a gray value f(x,y) of each pixel (x,y) in an input image is as an independent variable of a function, H denotes a transform operation performed on f(x,y) in a spatial domain to increase or reduce the gray value, to obtain a dependent variable as a gray value g(x,y) in an output image, and equation (1) is as followsperforming spatial smoothing filtering enhancement on a gray image by using a neighborhood averaging method of a spatial domain method to eliminate jagged contours due to uneven light, local highlighting, and metal reflection caused by a point light source in a real pipe environment, wherein weight of each pixel is equal in the neighborhood averaging method, considering that importance of each pixel is assumed to be same, and equation (2) is as follows:. A method for restoring video data of a pipe based on computer vision, comprising:wherein, s is a set of pixel coordinates in a neighborhood of (x,y), (i,j) is coordinates of a pixel in the ...

APPAREIL NETTOYEUR DE PISCINE A PILOTAGE OPTIMISE

The invention relates to a pool cleaning apparatus including: a cleaning unit (), at least one hydraulic system for circulating a liquid between at least one liquid inlet () and at least one liquid outlet () and through a filtration device of the cleaning unit (), a pump suitable for forcing the circulation of water between the liquid inlet () and the liquid outlet () and through the filtration device, drive and guide means for moving the cleaning unit, and means for controlling said drive and guide means. The pool cleaning apparatus includes at least one debris detection means placed in the hydraulic system of the cleaning unit (). 1. Appareil de nettoyage de piscine comprenant:{'b': '100', 'une unité de nettoyage (),'}{'b': 103', '104', '100, "au moins un circuit hydraulique de circulation de liquide entre au moins une entrée de liquide () et au moins une sortie de liquide (), à travers un dispositif de filtration de l'unité de nettoyage (),"}{'b': 103', '104, "une pompe adaptée à forcer une circulation d'eau entre l'entrée de liquide () et la sortie de liquide () au travers du dispositif de filtration,"}des moyens d'entrainement et de guidage pemettant d'assurer les mouvements de l'unite de nettoyage,{'b': '100', "des moyens de commande de ces moyens d'entrainement et de guidage, caractérisé en ce que l'appareil de nettoyage comprend au moins un moyean de detection de débris dispose dans le circuit hydraulique de l'unite de nettoyage ()."}217-. (canceled) The present invention relates to the field of equipment for swimming pools. It relates more specifically to a swimming pool cleaning apparatus including means for optimizing control so as to improve the cleaning of the pool.The invention relates to an apparatus for cleaning a surface submerged in a liquid, such as a surface formed by the walls of a pool, in particular of a swimming pool. It is in particular a mobile swimming pool cleaning robot. Such a cleaning robot carries out said cleaning by travelling over ...

AI SYSTEM FOR GENERATING MULTIPLE LABELS BASED ON A MEDICAL SCAN AND METHODS FOR USE THEREWITH

A global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device. 1. A global multi-label generating system , comprising:at least one processor; and generate a computer vision model by training on a plurality of medical scans and a plurality of labels corresponding to the plurality of medical scans, wherein each of the plurality of labels correspond to one of a set of abnormality classes;', 'generate global probability data based on utilizing the computer vision model on a new medical scan, wherein the global probability data indicates a set of global probability values corresponding to the set of abnormality classes, and wherein each of the set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in the new medical scan;', 'determine a subset of the set of abnormality classes are present in the new medical scan in response to a corresponding subset of the set of global probability values comparing favorably to a corresponding set of probability thresholds; and', 'transmit, via a transmitter, abnormality data that indicates the subset of the set of abnormality classes to a ...

IMAGE PROCESSING APPARATUS, OPERATION METHOD PERFORMED BY IMAGE PROCESSING APPARATUS AND RECORDING MEDIUM

An image processing apparatus includes: a processor comprising hardware, the processer being configured to execute: setting, in an image, an area of interest where classification is evaluated; calculating surface layer structure information representing a surface layer structure in the area of interest; calculating at least focus degrees of the outside of the area of interest in the image; and classifying the image based on the surface layer structure information and the focus degrees of the outside of the area of interest. 1. An image processing apparatus comprising: setting, in an image, an area of interest where classification is evaluated;', 'calculating surface layer structure information representing a surface layer structure in the area of interest;', 'calculating at least focus degrees of the outside of the area of interest in the image; and', 'classifying the image based on the surface layer structure information and the focus degrees of the outside of the area of interest., 'a processor comprising hardware, the processer being configured to execute2. The image processing apparatus according to claim 1 , wherein the processor is configured to classify the image as any one of a focused image having the surface layer structure claim 1 , a focused image without the surface layer structure and an unfocused image.3. The image processing apparatus according to claim 1 , wherein the processor is configured to calculate frequency information on the image and calculate the focus degrees of the outside of the area of interest based on the frequency information.4. The image processing apparatus according to claim 3 , wherein the processor is configured to calculate intensities of a specific frequency band of the image based on the frequency information and obtain the calculated intensities as the focus degrees of the outside of the area of interest.5. The image processing apparatus according to claim 1 , wherein the processor is configured tocalculate distances from ...

Relightable texture for use in rendering an image

Relightable free-viewpoint rendering allows a novel view of a scene to be rendered and relit based on multiple views of the scene from multiple camera viewpoints. An initial texture can be segmented into materials and an initial coarse colour estimate is determined for each material. Scene geometry is estimated from the captured views of the scene and is used to scale the initial coarse colour estimates relative to each other such that the different materials appear to be lit with a similar irradiance. In this way, a global irradiance function is estimated describing the scene illumination. This provides a starting point for a colour estimate and shading estimate extraction. The shading estimate can be used to fit surface normals to the global irradiance function. The set of surface normals and the colour estimate are stored for subsequent use to allow relighting of the scene. 1. A method of determining a colour component of a relightable texture for a scene , for use in rendering an image from a rendering viewpoint under arbitrary lighting conditions , the method comprising:obtaining an initial texture that is segmented into a plurality of materials, wherein an initial coarse colour estimate is obtained for each of the materials;determining one or more scale factors, for scaling a respective one or more of the initial coarse colour estimates, the scale factors being determined in dependence on irradiance estimates determined for the materials based on shading estimates which correspond with scaled versions of the initial coarse colour estimates of the materials;determining a global irradiance function for the scene using the determined scale factors; andusing the global irradiance function and the initial texture to determine the colour component of the relightable texture.2. The method of claim 1 , wherein for each of the materials claim 1 , the initial coarse colour estimate is determined by finding the average of the values in the initial texture for the ...

Systems and Methods for Hybrid Depth Regularization

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map. 1. A depth sensing system , comprising:a plurality of cameras;a processor;a memory containing an image processing application; obtain image data for a plurality of images from multiple viewpoints using the plurality of cameras, wherein the image data for the plurality of images comprises a reference image and at least one alternate view image;', 'generate a raw depth map containing depth estimates for pixels within the reference image using the image data for the reference image and the image data for the at least one alternate view image using a first depth estimation process, and a confidence map describing reliability of depth estimates contained within the raw depth map; and', computing a secondary depth map containing depth estimates for pixels within the reference image using a second different depth estimation process; and', 'computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map, where a depth estimate for a pixel in the reference image is selected from the raw depth map when the depth estimate is indicated as being ...

Multi-label heat map display system

A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes. An interactive interface that displays image data of the medical scan and at least one of the set of heat maps is generated for display on a display device associated with the multi-label heat map display system. User input to a client device is received, and an updated interactive interface that includes a change to the display of the at least one of the set of heat maps by the second portion of the interactive interface in response to the user input is displayed.

CONTRAST PARAMETER LEARNING SYSTEM

A contrast parameter learning system is operable to generate contrast significance data for a computer vision model, where the computer vision model was generated by performing a training step on a training set of medical scans. Significant contrast parameters are identified based on the contrast significance data. A re-contrasted training set is generated by performing an intensity transformation function that utilizes the significant contrast parameters on the training set of medical scans. A re-trained model is generated by performing the training step on the first re-contrasted training set. Re-contrasted image data of a new medical scan is generated by performing the intensity transformation function. Inference data is generated by performing an inference function that utilizes the first re-trained model on the re-contrasted image data. The inference data is transmitted via the transmitter to a client device for display via a display device. 1. A contrast parameter learning system , comprising:at least one processor; and generate first contrast significance data for a first computer vision model, wherein the computer vision model was generated by performing a training step on a first training set of medical scans;', 'identify first significant contrast parameters based on the first contrast significance data;', 'generating a first re-contrasted training set by performing a first intensity transformation function on the first training set of medical scans, wherein the first intensity transformation function utilizes the first significant contrast parameters;', 'generate a first re-trained model by performing the training step on the first re-contrasted training set;', 'receive, via a receiver, a new medical scan;', 'generate re-contrasted image data of the new medical scan by performing the first intensity transformation function;', 'generate inference data by performing an inference function that utilizes the first re-trained model on the re-contrasted image ...

Pool cleaning apparatus with optimized control

The invention relates to a pool cleaning apparatus including: a cleaning unit ( 100 ), at least one hydraulic system for circulating a liquid between at least one liquid inlet ( 103 ) and at least one liquid outlet ( 104 ) and through a filtration device of the cleaning unit ( 100 ), a pump suitable for forcing the circulation of water between the liquid inlet ( 103 ) and the liquid outlet ( 104 ) and through the filtration device, drive and guide means for moving the cleaning unit, and means for controlling said drive and guide means. The pool cleaning apparatus includes at least one debris detection means placed in the hydraulic system of the cleaning unit ( 100 ).

Triage routing system

A triage routing system is operable to receive a medical scan via a receiver. Inference data for the medical scan is generated by performing an inference function, where the inference function utilizes a computer-vision model trained on a plurality of medical scans. One of a plurality of medical professionals is selected to review the medical scan based on the inference data. Triage routing data that indicates the medical scan and the one of the plurality of medical professionals is generated. The medical scan is transmitted to a client device associated with the one of the plurality of medical professionals for display via a display device in accordance with the triage routing data.

Medical scan triaging system

A medical scan triaging system is operable to generate a global abnormality probability for each of a plurality of medical scans by utilizing a computer vision model trained on a training set of medical scans. A triage probability threshold is determined based on user input to a client device. A first subset of the plurality of medical scans, designated for human review, is determined by identifying medical scans with a corresponding global abnormality probability that compares favorably to the triage probability threshold. A second subset of the plurality of medical scans, designated as normal, is determined by identifying ones of the plurality of medical scans with a corresponding global abnormality probability that compares unfavorably to the triage probability threshold. Transmission of the first subset of the plurality of medical scans to a plurality of client devices associated with a plurality of users is facilitated.

SYSTEM AND METHOD FOR IMAGE PROCESSING

A system and method for image processing are provided. A pre-processed image may be obtained. The pre-processed image may be decomposed into a low-frequency image and a high-frequency image. At least one grayscale transformation range may be determined based on the low-frequency image. At least one grayscale transformation parameter may be determined based on the at least one grayscale transformation range. The low-frequency image may be transformed based on the at least one grayscale transformation parameter to obtain a transformed low-frequency image. A transformed image may be generated by reconstructing the transformed low-frequency image and the high-frequency image. 1. A system , comprising:at least one storage device including a set of instructions or programs; and obtain a pre-processed image;', 'decompose the pre-processed image into a low-frequency image and a high-frequency image;', 'determine at least one grayscale transformation range based on the low-frequency image;', 'determine at least one grayscale transformation parameter based on the at least one grayscale transformation range;', 'obtain a transformed low-frequency image by transforming the low-frequency image based on the at least one grayscale transformation parameter; and', 'generate a transformed image by reconstructing the transformed low-frequency image and the high-frequency image., 'at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions or programs, the at least one processor is configured to cause the system to2. The system of claim 1 , wherein to obtain the pre-processed image claim 1 , the at least one processor is configured to cause the system to:obtain the pre-processed image by performing a logarithmic transformation on an initial image.3. The system of claim 1 , wherein to obtain the pre-processed image claim 1 , the at least one processor is configured to cause the system to:obtain a segmented image by ...

SYSTEMS AND METHODS FOR PATIENT STRUCTURE ESTIMATION DURING MEDICAL IMAGING

Methods and systems are provided for estimating patient structure prior to a scan by a medical imaging system. As one example, a method may include acquiring depth images of a patient positioned on a table of the medical imaging system, correcting the depth images based on histogram data from the depth images, and extracting a three-dimensional structure of the patient based on the corrected depth images. 1. A method for a medical imaging system , comprising:acquiring depth images of a patient positioned on a table of the medical imaging system via a depth camera;correcting the depth images based on histogram data from the depth images; andextracting a three-dimensional structure of the patient based on the corrected depth images.2. The method of claim 1 , further comprising:determining a pose of the patient from the extracted three-dimensional structure of the patient;comparing the determined pose of the patient with a desired patient pose; andoutputting an alert responsive to the determined pose of the patient not matching the desired patient pose.3. The method of claim 1 , wherein the table is movable with respect to a gantry bore claim 1 , and the method further comprises:determining a shape of the patient from the extracted three-dimensional structure of the patient;performing an scan prediction based on the determined shape of the patient relative to coordinates of the table; andoutputting an alert responsive to the scan prediction indicating contact between the patient the gantry bore.4. The method of claim 1 , wherein correcting the depth images based on the histogram data from the depth images includes performing a coefficient of illumination (CoIV)-based correction and is further based on histogram data from color images of the patient.5. The method of claim 4 , wherein performing the CoIV-based correction includes:determining a standard deviation of combined histogram data from both the depth images and the color images and a CoIV of the combined ...

Method and apparatus for modeling smoke turbulence based on patch

A method and apparatus for modeling smoke turbulence is disclosed. The method of modeling smoke turbulence includes detecting a turbulent area comprising smoke turbulence in a low-resolution image, and searching for a high-resolution patch matching the turbulent area in a memory that comprises high-resolution patches representing smoke turbulence extracted from high-resolution images. The method also models the smoke turbulence by synthesizing the retrieved patch to the low-resolution image.

ANGLE- DEPENDENT ANISOTROPIC FILTERING

A method for anisotropic filtering is provided herein. The method includes computing an anisotropic filter with a major-axis and a minor-axis for a pixel to be displayed on screen-space, wherein the anisotropic filter is to be applied to corresponding MIPs on a texture map. The method includes varying the length of the major-axis of the anisotropic filter based on the angle of the major-axis of anisotropy with respect to the screen space. The method includes determining a number of texels from the texture map that are to be sampled in the anisotropic filter based on the length of the modified major-axis. The method includes determining the color of the pixel based on the texels sampled in the anisotropic filter. 1. A graphics processing unit , comprising:at least one bilinear low pass filter, wherein the bilinear low pass filter is configured to filter data for a first MIP map and a second MIP map, where data from each MIP map represents a two by two group of texels, wherein the two by two group of texels is nearest to a desired position on the first MIP map and the second MIP map, respectively;at least one anisotropic low pass filter, wherein the anisotropic low pass filter is configured to adaptively filter sampled data from the bilinear low pass filter for the first MIP map and the second MIP map, and a length of a major-axis of the anisotropic low pass filter is varied based on a screen space angle; andat least one sampler unit, wherein the sampler unit is configured to sample the output of the bilinear low pass filter and the anisotropic low pass filter.2. The graphics processing unit of claim 1 , wherein the results of the anisotropic sampling on the first MIP map and the second MIP map are combined as a weighted sum based on a fractional part.3. The graphics processing unit of claim 1 , wherein an effective number of ways is computed for each of the first MIP map and the second MIP map claim 1 , and each of the first MIP map and the second MIP map is sampled ...

Image processing of images that include marker images

An image processing method, includes: obtaining an image, the image having marker images and a background image; identifying presence of an object in the background image using a processor; and providing a signal for stopping a procedure if the presence of the object is identified. An image processing apparatus, includes: a processor configured for: obtaining an image, the image having marker images and a background image; identifying presence of an object in the background image; and providing a signal for stopping a procedure if the presence of the object is identified. A computer product having a non-transitory medium storing a set of instructions, an execution of which causes an image processing method to be performed, the method includes: obtaining an image, the image having marker images and a background image; identifying presence of an object in the background image; and providing a signal for stopping a procedure if the presence of the object is identified.

SYSTEM AND METHOD FOR QUANTIFYING NOZZLE OCCLUSION IN 3D PRINTING

One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer. 1. A computer-executed method for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets , the method comprising:capturing, by one or more cameras, an image of the orifice of the 3D printer nozzle while the 3D printer nozzle is jetting liquid droplets;performing an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle;computing an occlusion fraction based on the determined occluded regions; andgenerating an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.2. The computer-executed method of claim 1 , wherein performing the image-analysis operation comprises computing a gray-level co-occurrence matrix (GLCM) for the captured image to obtain a texture-analysis outcome associated with the captured image.3. The computer-executed method of claim 2 , wherein computing the GLCM further comprises applying a 4×2 offset matrix on pixels of the captured image.4. The computer-executed method of claim 3 , wherein the 4×2 offset matrix comprises matrix [0 claim 3 ,1; −1 claim 3 ,1; −1 claim 3 ,0; −1 claim 3 ,−1].5. The computer-executed method of claim 2 , further comprising:applying an adaptive thresholding ...

METHOD, APPARATUS AND COMPUTER PROGRAM FOR ANALYSING MEDICAL IMAGE DATA

Medical image data is analysed to produce a biomarker. The data is filtered with a plurality of band-pass filters each having a different bandwidth. A texture parameter is then determined from the filtered data from each filter and the biomarker is determined as at a ratio of the texture parameters. When the biomarker is obtained from a CT image of a liver, it can be predictive of poor survival, disease extent and liver physiology of a patient following resection of colorectal cancer. When obtained from a mammographic image, the biomarker can be indicative of cancer invasion and receptor status within mammographic abnormalities. When obtained from a CT image of a lung nodule, the biomarker can be predictive of tumour stage (or grading) and tumour metabolism of a patient with non-small cell lung carcinoma (lung cancer). 1. A computer-implemented method of producing an imaging biomarker of cancer in an organ of a subject , the method comprising:receiving medical image data representing a computed tomography (CT) image of the subject's organ;generating first filtered image data representing a filtered image of the subject's organ by filtering the received medical image data with a first non-orthogonal wavelet transform band-pass filter, wherein the first non-orthogonal wavelet transform band-pass filter has a width in a spatial domain that is sensitive to objects having a size in a range from about 2 pixels/voxels to about 12 pixels/voxels;generating image histogram data representing an image histogram of a region of interest contained in the received medical image from the first filtered image data;computing a first texture parameter from the generated image histogram data, wherein the first texture parameter is at least one of a mean gray-level intensity, an indicator of entropy, or a uniformity of the generated image histogram data; andsetting a value of the imaging biomarker to the first texture parameter, wherein the imaging biomarker is a diagnostic or prognostic ...

APPARATUS AND METHOD FOR PROCESSING TEXTURED IMAGE

Disclosed herein are an apparatus and method for processing a textured image. The apparatus includes a filter unit for detecting an edge of an input image and transforming the input image into an image in which a density of the edge is represented; a smoothing unit for removing noise from the transformed image and smoothing the image; a clustering unit for changing a number of regions into which the smoothed image is to be segmented and clustering the smoothed image a preset number of times; and a cluster optimization unit for setting a final number of clusters for the input image by optimizing a number of clusters based on a previously learned ground truth, for selecting an image corresponding to the final number of clusters from results of clustering by which the image is segmented into a different number of regions, and for outputting the selected image. 1. An apparatus for processing a textured image , comprising:a filter unit for detecting an edge of an input image and transforming the input image into an image in which a density of the edge is represented using information about the detected edge;a smoothing unit for removing noise from the transformed image and then smoothing the image;a clustering unit for performing, a preset number of times, changing a number of regions into which the smoothed image is to be segmented and clustering the smoothed image; anda cluster optimization unit for determining a final number of clusters for the input image by optimizing a number of clusters for the input image based on a previously learned ground truth, for selecting a resultant image corresponding to the final number of clusters from among results of clustering by which the image is segmented into a different number of regions, and for outputting the selected resultant image.2. The apparatus of claim 1 , wherein the cluster optimization unit determines the number of clusters corresponding to any one of the results of clustering based on an energy value for each of ...

Background removal

A method of displaying a portion of a captured scene may include visually capturing a scene at a mobile device. An area of the captured scene associated with a foreground object of the captured scene may be identified at the mobile device. The mobile device may display, in real time, a displayed scene including a foreground portion of the captured image associated with the area identified as being associated with the foreground object of the captured scene. The displayed scene may further include a background different from a background portion of the captured image not associated with the area identified as being associated with the foreground object of the captured scene. The displayed scene may demonstrate an expected result of a separate background removal process.

METHOD AND IMAGE PROCESSING ENTITY FOR APPLYING A CONVOLUTIONAL NEURAL NETWORK TO AN IMAGE

A method and an image processing entity for applying a convolutional neural network to an image are disclosed. The image processing entity processes the image while using the convolutional kernel to render a feature map, whereby a second feature map size of the feature map is greater than a first feature map size of the feature maps with which the feature kernel was trained. Furthermore, the image processing entity repeatedly applies the feature kernel to the feature map in a stepwise manner, wherein the feature kernel was trained to identify the feature based on the feature maps of the first feature maps, wherein the feature kernel has the first feature map size. 1. A method for applying a convolutional neural network to an image , wherein the convolutional neural network comprises a convolutional kernel for convolving with images of a first image size to render feature maps and a feature kernel trained to identify a feature in the images based on the feature maps , wherein the first image size is less than a second image size of the image to which the convolutional neural network is applied , wherein the method comprises:processing the image while using the convolutional kernel to render a feature map, whereby a second feature map size of the feature map is greater than a first feature map size of the feature maps with which the feature kernel was trained, andrepeatedly applying the feature kernel to the feature map in a stepwise manner, referring to displacement of consecutive applications of the feature kernel to the feature map, wherein the feature kernel was trained to identify the feature based on the feature maps of the first feature map size, wherein the feature kernel has the first feature map size, wherein the feature maps were obtained by convolving the convolutional kernel over images having the first image size, which causes, at least due to the convolution, the feature map to have the second feature map size, characterized in that the stepwise manner ...

Intelligent police car loudspeaker

An intelligent police car loudspeaker includes a loudspeaker mechanism composed of a dust cover, a paper cone, a yoke ring, a centering piece, a magnet, a voice coil, and a soft iron core. The voice coil is disposed around the soft iron core. The magnet is disposed around the voice coil. The paper cone is disposed above the magnet. The centering piece is disposed above the magnet and located inside the paper cone. The yoke ring is disposed above the centering piece and located inside the paper cone. The dust cover is configured to cover an opening of the paper cone. With the present invention, the level of intelligence of the loudspeaker can be improved.

Heat map display system and methods for use therewith

A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes. An interactive interface that displays image data of the medical scan and at least one of the set of heat maps is generated for display on a display device associated with the multi-label heat map display system. User input to a client device is received, and an updated interactive interface that includes a change to the display of the at least one of the set of heat maps by the second portion of the interactive interface in response to the user input is displayed.

OBJECT DETECTING APPARATUS, OBJECT DETECTING METHOD, AND COMPUTER PROGRAM PRODUCT

According to an embodiment, an object detecting apparatus includes a detector and an estimation processing unit. The detector is configured to detect an object candidate area from a captured image. The estimating processing unit is configured to, by performing estimation processing using a part of or a whole of the captured image including at least the object candidate area, output object information including at least information representing a pose of an object in the object candidate area. The estimation processing includes: a first process estimating, from among vertices of a cuboid circumscribing the object and making contact with a road surface, positions of at least two vertices on the captured image that are viewable from a viewpoint of the captured image; and a second process estimating to which one of right-front, left-front, right-rear, or left-rear of the object the vertices having positions estimated by the first process are respectively corresponding. 1. An object detecting apparatus , comprising:a detector configured to detect an object candidate area from a captured image; andan estimation processing unit configured to, by performing estimation processing using a part of or a whole of the captured image including at least the object candidate area, output object information including at least information representing a pose of an object in the object candidate area, wherein a first process estimating, from among vertices of a cuboid circumscribing the object and making contact with a road surface, positions of at least two vertices on the captured image that are viewable from a viewpoint of the captured image; and', 'a second process estimating to which one of right-front, left-front, right-rear, or left-rear of the object the vertices having positions estimated by the first process are respectively corresponding., 'the estimation processing includes2. The apparatus according to claim 1 , whereinthe first process estimates, from among vertices of a ...

DUAL SENSOR IMAGING SYSTEM AND DEPTH MAP CALCULATION METHOD THEREOF

A dual sensor imaging system and a depth map calculation method thereof are provided. The dual sensor imaging system includes at least one color sensor, at least one infrared ray (IR) sensor, a storage device, and a processor. The processor is configured to load and execute a computer program stored in the storage device to: control the color sensor and the IR sensor to respectively capture multiple color images and multiple IR images by adopting multiple exposure conditions suitable for an imaging scene, adaptively select a combination of the color image and the IR image that are comparable to each other from the color images and the IR images; and calculate a depth map of the imaging scene by using the selected color image and IR image. 1. A dual sensor imaging system , comprising:at least one color sensor;at least one infrared ray (IR) sensor;a storage device, storing a computer program; and control the at least one color sensor and the at least one IR sensor to respectively capture a plurality of color images and a plurality of IR images by adopting a plurality of exposure conditions suitable for an imaging scene;', 'adaptively select a combination of the color image and the IR image that are comparable to each other from the color images and the IR images; and', 'calculate a depth map of the imaging scene by using the selected color image and IR image., 'a processer, coupled to the at least one color sensor, the at least one IR sensor, and the storage device, and configured to load and execute the computer program to2. The dual sensor imaging system according to claim 1 , wherein the processor:selects a color image that can reveal color details of the imaging scene from the color images as a reference image;identifies at least one defect area lacking texture details in the reference image; andselects an IR image that can reveal the texture details of the defect area from the IR images to be used as a combination for comparison with the reference image.3. The ...

INFORMATION PROCESSING APPARATUS AND METHOD

An information processing method comprises: transmitting first image data to an external image processing apparatus and receiving second image data processed by the external image processing apparatus; extracting a feature amount according to a content of image processing performed by the external image processing apparatus from the first image data; and converting the extracted feature amount to displayable information and displaying the information on a display. 1. An information processing method comprising:transmitting first image data to an external image processing apparatus and receiving second image data processed by the external image processing apparatus;extracting a feature amount according to a content of image processing performed by the external image processing apparatus from the first image data; andconverting the extracted feature amount to displayable information and displaying the information on a display.2. The information processing method according to claim 1 , further comprising:receiving, in a case where the image processing by the external image processing apparatus is changed, information on changed image processing;changing the feature amount to be extracted according to the information on the changed image processing.3. The information processing method according to claim 1 , wherein the information is displayed on the display by being superimposed on the first image data.4. The information processing method according to claim 1 , wherein the first image data and the information are displayed separately on the display.5. The information processing method according to claim 1 , wherein the information is on a region where the feature amount is equal to or greater than a first threshold.6. The information processing method according to claim 1 , wherein the information indicates that the effect to be expected by the image processing is high in a case where a number of pixels included in a region where the feature amount is equal to or ...

EFFICIENT PARALLEL COMPUTING METHOD FOR BOX FILTER

An efficient parallel computing method for a box filter, includes: step 1, with respect to a given degree of parallelism N and a radius r of the filter kernel, establishing a first architecture provided without an extra register and a second architecture provided with the extra register; step 2, building a first adder tree for the first architecture and a second adder tree for the second architecture, respectively; step 3, searching the first adder tree and the second adder tree from top to bottom, calculating the pixel average corresponding to each filter kernel by using the first adder tree and the second adder tree, respectively, and counting resources required to be consumed by the first architecture and the second architecture, respectively; and, step 4, selecting one architecture consuming a relatively small resources from the first architecture and the second architecture for computing the box filter. 1. A parallel computing method for a box filter , wherein , a filter kernel is initially located on an upper left corner of the box filter , and moves from left to right and from top to bottom; assuming that a radius of the filter kernel is r , the filter kernel consists of (2r+1) columns of pixels , and each column of pixels of the (2r+1) columns of pixels consist of (2r+1) rows of pixels; the filter kernel moves by one column each time when moving from left to right , and moves by one row each time when moving from top to bottom; an average of all pixels within the filter kernel is defined as a pixel average; the pixel average corresponding to the filter kernel after each movement is calculated , and the pixel averages corresponding to all filter kernels are taken as a computing result of the box filter; the averages corresponding to all the filter kernels are calculated by the parallel computing method comprising the following steps:step 1: establishing two parallel architectures with respect to a given degree of parallelism N and the radius r of the filter ...

SYSTEM AND METHOD FOR SMART-IMAGE CAPTURING

One embodiment can include a system for providing an image-capturing recommendation. During operation the system receives, from a mobile computing device, one or more images. The one or more images are captured by one or more cameras associated with the mobile computing device. The system analyzes the received images to obtain image-capturing conditions for capturing images of a target within a physical space; determines, based on the obtained image-capturing conditions and a predetermined image-quality requirement, one or more image-capturing settings; and recommends the determined one or more image-capturing settings to a user. 1. A computer-executed method for providing an image-capturing recommendation , the method comprising:receiving, from a mobile computing device, one or more images, wherein the one or more images are captured by one or more cameras associated with the mobile computing device;analyzing the received images to obtain image-capturing conditions for capturing images of a target within a physical space;determining, based on the obtained image-capturing conditions and a predetermined image-quality requirement, one or more image-capturing settings; andrecommending the determined one or more image-capturing settings to a user.2. The method of claim 1 , wherein the one or more images comprise images of the physical space claim 1 , images of the target claim 1 , or both.3. The method of claim 2 , wherein the mobile computing device comprises at least two cameras configured to capture claim 2 , simultaneously claim 2 , an image of the physical space and an image of the target.4. The method of claim 1 , further comprising receiving metadata associated with a respective image claim 1 , wherein obtaining the image-capturing conditions comprises analyzing the metadata.5. The method of claim 1 , wherein a respective image-capturing setting comprises at least one of:a location;a time; andcamera geometry.6. The method of claim 1 , further comprising: ...

MAMMOGRAPHY APPARATUS

A method of diagnosing breast cancer, the method comprising: acquiring a contrast enhanced region of interest (CE-ROI) comprised in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; generating a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI; and using a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant. 1. A method of diagnosing breast cancer , the method comprising:acquiring a contrast enhanced region of interest (CE-ROI) comprised in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image;determining a texture neighborhood for each of the plurality of X-ray pixels, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel;generating a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; andusing a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.2. (canceled)3. The method according to wherein BPR is greater than or equal to about 25 claim 1 , 50 claim 1 , or 75.45-. (canceled)6. The method according to and comprising selecting a plurality of R different pixel radii PR(r) claim 1 , 1≤r≤R claim 1 , in the texture neighborhood claim 1 , for which PR(r)≤PR(r+1) and PR(R)=BPR.7. The method according to wherein R is greater than or equal to 2 claim 6 , 10 claim 6 , or 20.89-. (canceled)109. The method according to claim and comprising for each pixel radius PR(r) selecting a number J(r) of X-ray pixels from the X-ray pixels at the pixel ...

Nuclear Medical Image Analysis Technique

One embodiment of the present invention addresses the problem of reducing the influence of physiological accumulation in monitoring nuclear medical image data. To solve this problem, the embodiment includes: extracting a bone area from CT image data having been positioned with nuclear medical image data; in the nuclear medical image data, displaying the data of an area overlapping the bone area extracted above; and, in the nuclear medical image data, not displaying the data of an area not overlapping the bone area extracted above. 120.-. (canceled)21. A computer readable medium having a stored computer program for automatically extracting a tumor contour from three-dimensional nuclear medicine image data , the computer program comprising a first program instruction group that is configured , when executed by at least one processor of a system , to cause the system to execute:setting a reference point in a region including a tumor on the nuclear medicine image data;scanning pixels radially and three-dimensionally from the reference point, and creating a pixel value profile in each scanning direction;creating a gradient strength profile for the pixel value profile or for the pixel value profile to which smoothing has been applied;setting a contour identification range in the pixel value profile or in the pixel value profile to which smoothing has been applied; anddetermining a tumor contour point in the pixel value profile within the contour identification range, based on a peak having the largest kurtosis in the corresponding gradient strength profile or in the corresponding gradient strength profile to which smoothing has been applied.22. The computer readable medium according to claim 21 , wherein the setting of the contour identification range is performed based on an extreme point in the pixel value profile or in the pixel value profile to which smoothing has been applied.23. The computer readable medium according to claim 21 , wherein the kurtosis is a cosine of ...

TIMING CONTROLLER AND OPERATING METHOD THEREOF

A timing controller includes an image compensator for generating compensated image data. The image compensator is configured to: divide an input image into plural image blocks; select plural pixels located at one column of the image block as plural target pixels; generate an average representative gray level of the image block according to a histogram of gray levels of the image block; input the average representative gray level into a first lookup table to obtain a first gain; input a vertical pixel position of the target pixel and the average representative gray level into a second lookup table to obtain a second gain; obtain the compensated gray level by multiplying the gray level, the first gain, and the second gain of the target pixel; and replace the gray levels with the compensated gray levels to acquire the compensated image data. 1. A timing controller , comprising:a receiver configured to receive image data of an input image, wherein the input image comprises a plurality of pixels;an image compensator configured to generate compensated image data according to the image data; anda transmitter configured to output the compensated image data such that a display device comprising the timing controller displays an image according to the compensated image data; divide the input image into a plurality of image blocks along a horizontal direction;', 'select the pixels located at one of columns of each of the image blocks as a plurality of target pixels;', 'obtain each of gray levels of the pixels;', 'generate an average representative gray level of each of the image blocks according to a histogram of the gray levels of each of the image blocks;', 'input the average representative gray level of each of the image blocks into a first lookup table to obtain a first gain of corresponding image block;', 'input a vertical pixel position of each of the target pixels of each of the image blocks and the average representative gray level of the corresponding image blocks ...

GENERATING A BREATHING ALERT

For generating a breathing alert is disclosed, a method receives a video stream of a subject. The method further estimates a breathing signal from the video stream. The method determines one of a large-scale motion and/or a breathing event of the subject based on the breathing signal. The method generates an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval. 1. A method comprising:receiving, by a processing apparatus, a video stream of a subject;estimating, by the processing apparatus, a breathing signal from the video stream;determining, by the processing apparatus, one of a large-scale motion and/or a breathing event of the subject based on the breathing signal; andgenerating, by the processing apparatus, an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval.2. The method of claim 1 , wherein estimating the breathing signal comprises:filtering, by the processing apparatus, non-moving objects from each image frame to yield a removed image;decomposing, by the processing apparatus, each removed image into a plurality of frame regions, wherein a first frame region covers a feature of the subject;calculating, by the processing apparatus, a pixel weight for each pixel of each frame region, wherein each pixel weight is one of a positive weight or a negative scalar;coherently combining, by the processing apparatus, the pixel weights for each frame region as a scalar time signal for each frame region;calculating, by the processing apparatus, a low order subspace decomposition of the scalar time signal; andestimating, by the processing apparatus, a decomposition frequency of the low order subspace decomposition for each frame region as the breathing signal.3. The method of claim 2 , wherein the breathing signal is estimated using a logistic regression classifier claim 2 , and the logistic regression classifier is ...

Aerial image segmentation for refineries

A system receives a two-dimensional digital image of an aerial industrial plant area. Based on requirements of image processing, the image is zoomed in to different sub-images, that are referred to as first images. The system identifies circular tanks, vegetation areas, process areas, and buildings in the first image. The system formulates a second digital image by concatenating the first images. The system creates one or more polygons of the regions segmented in the second digital image. Each polygon encompasses a tank area, a vegetation area, a process area, or a building area in the second digital image, which is a concatenated image of the individual regions. The system displays the second digital image on a computer display device.

REGION OF INTEREST HISTOGRAM PROCESSING FOR IMPROVED PICTURE ENHANCEMENT

Methods, systems, and devices implementing region of interest (ROI) histograms for display processing (e.g., for display adjustment) are described. A display processing component may determine a histogram based on the ROI (e.g., based on gray levels corresponding to pixels within a frame or image aspect ratio) by removing or otherwise not considering background filler (e.g., background, such as solid black color, that may be added by a device to fill a panel area when an image or frame aspect ratio is not equivalent to the display panel aspect ratio). Source pipe programming (e.g., display information read from a display hardware pipeline and ROI information read from a video or image pipeline) may be used for improved histogram determination or histogram modification. A histogram for processing block operations may therefore be determined based on (e.g., or modified to reflect) the ROI corresponding to the image or frame to be displayed. 1. A method for adjusting a display at a device , comprising:identifying an image based at least in part on source surface pipe programming;selecting a region of interest that is a subset of a display panel that comprises the image based at least in part on the source surface pipe programming;determining a first histogram that is based at least in part on gray level values of the region of interest; andadjusting one or more gray levels of the region of interest based at least in part on the first histogram.2. The method of claim 1 , further comprising:identifying a number of pixels of a background region of the display and a gray level value that corresponds to the background region based at least in part on the source surface pipe programming, wherein the first histogram is determined based at least in part on the number of pixels and the gray level value.3. The method of claim 1 , further comprising:identifying an enable signal from the source surface pipe programming; andidentifying the region of interest based at least in part ...