Method and apparatus for processing an image comprising characters

Method and apparatus for processing an image including a character are disclosed. The method may include: searching in a set of characters one or more characters having highest similarities of shape to a character in the set of characters, hereinafter the character being referred to as a first character, the one or more searched characters forming a similar character list of the first character; searching in the set of characters one or more characters having highest similarities of shape to each character in the similar character list of the first character, to form a similar character list of each character in the similar character list of the first character; and selecting in the similar character lists one or more characters having a high mutual similarity between each other, as a character cluster.

Image processing system and method

A method dividing an image into plural superpixels of plural pixels of the image. The method calculates an initial set of weights from a measure of similarity between pairs of pixels, from which a resultant set of weights is calculated for pairs of pixels that are less that a threshold distance apart on the image. The calculation calculates a weight for a pair of pixels as the sum over a set of third pixels of the product of initial weight of the first pixel of the pair of pixel with the third pixel and the weight of the third pixel with the second pixel. Each weight is then subjected to a power coefficient operation. The resultant set of weights and the initial set of weights are then compared to check for convergence. If the weights converge, the converged set of weights is used to divide the image into superpixels.

Classification and Organization of Consumer Digital Images Using Workflow, and Face Detection and Recognition

A processor-based system operating according to digitally-embedded programming instructions performs a method including identifying a group of pixels corresponding to a face region within digital image data acquired by an image acquisition device. A set of face analysis parameter values is extracted from said face region, including a faceprint associated with the face region. First and second reference faceprints are determined for a person using reference images captured respectively in predetermined face-portrait conditions and using ambient conditions. The faceprints are analyzed to determine a baseline faceprint and a range of variability from the baseline associated with the person. Results of the analyzing are stored and used in subsequent recognition of the person in a subsequent image acquired under ambient conditions.

Image Classification

The present disclosure introduces a method and an apparatus for classifying images. Classification image features of an image for classification are extracted. Based on a similarity relationship between each classification image feature and one or more visual words in a pre-generated visual dictionary, each classification image feature is quantified by multiple visual words in the visual dictionary and a similarity coefficient between each classification image feature and each of the visual words is determined. Based on the similarity coefficient of each visual word that corresponds to different classification image features, a weight of each visual word is determined to establish a classification visual word histogram of the image for classification. The classification visual word histogram is input into an image classifier that is trained by sample visual word histograms arising from multiple sample images. An output result is used to determine a classification of the image for classification.

Image concealing via efficient feature selection

A novel technique for unsupervised feature selection is disclosed. The disclosed methods include automatically selecting a subset of a feature of an image. Additionally, the selection of the subset of features may be incorporated with a congealing algorithm, such as a least-square-based congealing algorithm. By selecting a subset of the feature representation of an image, redundant and/or irrelevant features may be reduced or removed, and the efficiency and accuracy of least-square-based congealing may be improved.

Image classification apparatus and recording medium having program recorded therein

An image-classification apparatus includes: a first feature extraction unit to acquire a feature value of each of block images obtained by segmenting an input image; an area-segmentation unit to assign each of the block images to any one of K areas based on the feature value; a second feature extraction unit to acquire, based on an area-segmentation result, a feature vector whose elements including, the number of adjacent spots, each including two block images adjacent to each other in the input image, for each combination of the areas whereto the two block images are assigned; or a ratio of the number of block images assigned to each of the K areas to all the number of block images adjacent to a block image assigned to each of the K areas; and a classification unit to classify to which of a plurality of categories the input image belong.

Detecting facial similarity based on human perception of facial similarity

Similar faces may be determined within images based on human perception of facial similarity. The user may provide an image including a query face to which the user wishes to find faces that are similar. Similar faces may be determined based on similarity information. Similarity information may be generated from information related to a human perception of facial similarity. Images that include faces determined to be similar, based on the similarity information, may be provided to the user as search result images. The user then may provide feedback to indicate the user's perception of similarity between the query face and the search result images.

System and method for biometric authentication in connection with camera equipped devices

The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Information processing system, method for controlling information processing system, program, and information recording medium

Provided is an information processing system capable of grouping objects without needing to pre-define a group associated with a sorting condition. A determination unit determines whether or not one of a first object and a second object different from the first object is included in a determination region that is set based on a position of another one of the first object and the second object. A grouping unit groups the first object and the second object based on a result of a comparison between the attribute information associated with the first object and the attribute information associated with the second object in a case where it is determined that the one of the first object and the second object is included in the determination region.

Distance estimation to objects and free-space boundaries in autonomous machine applications

In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.

Image product creation based on face images grouped using image product statistics

A computer-implemented method for creating an image product by accurately grouping faces includes receiving an initial set of face groups for a plurality of face images, training classifiers between pairs of face groups in the initial set of face groups using image-product statistics by a computer processor, classifying the plurality of face images by classifiers to output binary vectors for the plurality of face images by the computer processor, calculating a value for an improved similarity function using the binary vectors for each pair of the plurality of face images, grouping the plurality of face images into modified face groups based on values of the binary similarity functions by the computer processor, and creating an image product based at least in part on the modified face groups

Method for Distinguishing Components of an Acoustic Signal

A method distinguishes components of a signal by processing the signal to estimate a set of analysis features, wherein each analysis feature defines an element of the signal and has feature values that represent parts of the signal, processing the signal to estimate input features of the signal, and processing the input features using a deep neural network to assign an associative descriptor to each element of the signal, wherein a degree of similarity between the associative descriptors of different elements is related to a degree to which the parts of the signal represented by the elements belong to a single component of the signal. The similarities between associative descriptors are processed to estimate correspondences between the elements of the signal and the components in the signal. Then, the signal is processed using the correspondences to distinguish component parts of the signal.

Clustering for k-anonymity in location trajectory data

An apparatus for providing anonymity in geographic data for probe devices in a geographic region for a location-based service includes at least a database, a clustering calculator and an anonymity controller. The database stores trajectory data based on sequences of sensor measurements of the probe devices. The clustering calculator clusters the trajectory data, according to a first iteration threshold, into clusters each defined by a cluster point and compares distance for a first cluster from the clusters to cluster points of other clusters of the clusters. The clustering calculator selects a second cluster from the clusters based on the comparison of distances and merges the first cluster and the second cluster into a merged cluster. The anonymity controller modifies the trajectory data to provide a predetermined level of anonymity to locations from the trajectory data in response to the merged cluster.

Gesture recognition method and apparatus based on analysis of multiple candidate boundaries

An image processing system comprises an image processor configured to identify a plurality of candidate boundaries in an image, to obtain corresponding modified images for respective ones of the candidate boundaries, to apply a mapping function to each of the modified images to generate a corresponding vector, to determine sets of estimates for respective ones of the vectors relative to designated class parameters, and to select a particular one of the candidate boundaries based on the sets of estimates. The designated class parameters may include sets of class parameters for respective ones of a plurality of classes each corresponding to a different gesture to be recognized. The candidate boundaries may comprise candidate palm boundaries associated with a hand in the image. The image processor may be further configured to select a particular one of the plurality of classes to recognize the corresponding gesture based on the sets of estimates.

Presentation and organization of content

Embodiments are provided for organization and presentation of content. In some embodiments, a plurality of images and a plurality of similarity rules for image categorization are received. For each image in the plurality of images, that image and each remaining image from the plurality is compared by: applying each similarity rule to the image and a remaining image from the plurality to obtain a numeric result, and recording the numeric result for the pair of images in a numeric representation, the numeric representation embodying similarities. The numeric representation is used as a reference for clustering the plurality of images into clusters of similar images, and each image is stored with a marker denoting a cluster to which it has been assigned.

Systems and methods to process electronic images to provide image-based cell group targeting

Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

Methods and systems for characterizing tissue of a subject utilizing a machine learning

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

Method for discovering relationships in data by dynamic quantum clustering

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Systems and methods for real-time complex character animations and interactivity

Systems, methods, and non-transitory computer-readable media can identify a virtual deformable geometric model to be animated in a real-time immersive environment. The virtual deformable geometric model comprises a virtual model mesh comprising a plurality of vertices, a plurality of edges, and a plurality of faces. The virtual model mesh is iteratively refined in one or more iterations to generate a refined mesh. Each iteration of the one or more iterations increases the number of vertices, the number of edges, and/or the number of faces. The refined mesh is presented during real-time animation of the virtual deformable geometric model within the real-time immersive environment.

Systems and methods for real-time complex character animations and interactivity

Systems, methods, and non-transitory computer-readable media can receive virtual model information associated with a virtual deformable geometric model. The virtual model information comprises a complex rig comprising a plurality of transforms and a first plurality of vertices defined by a default model, and a simplified rig comprising a second plurality of transforms and a second plurality of vertices. The second plurality of vertices correspond to the first plurality of vertices defined by the default model. The simplified rig and the complex rig are deformed based on an animation to be applied to the virtual deformable geometric model. A set of offset data is calculated. The set of offset data comprises, for each vertex in the first plurality of vertices, an offset between the vertex and a corresponding vertex in the second plurality of vertices.

Pose space dimensionality reduction for pose space deformation of a virtual character

Systems and methods for reducing pose space dimensionality. A plurality of example poses can define an input pose space. Each of the example poses can include a set of joint rotations for a virtual character. The joint rotations can be expressed with a singularity-free mathematical representation. The plurality of example poses can then be clustered into one or more clusters. A representative pose can be determined for each cluster. An output pose space with a reduced dimensionality, as compared to the input pose space, can then be provided.

Systems and methods for automated segmentation of retinal fluid in optical coherence tomography

Disclosed herein are methods and systems for the identification and characterization of fluid accumulation in the retina using OCT imaging. The disclosed methods and systems are directed to the automated segmentation of retinal fluid using 2D or 3D structural OCT scan images. Approaches for visualization and quantification of both intraretinal and subretinal fluid are presented. Methods are also disclosed for using OCT angiography data to improve the quality of retinal fluid segmentation, and to provide combined visualization of fluid accumulation and retinal vasculature to inform clinical interpretation of results.

Systems and methods for identifying electronic content using video graphs

Systems and methods are provided for identifying and recommending electronic content to consumers. In accordance with an implementation, one or more elements of electronic content are identified based on video graph data. In an exemplary method, information associated with a first element of video content is received, and corresponding video graph data is obtained. One or more second elements of video content that are similar to the first element of video content are identified based on the obtained video graph data. A subset the first and second elements of video content is subsequently identified for delivery to the user.

Automatic image product creation for user accounts comprising large number of images

A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Methods and Apparatuses for Detection of Abnormalities in Low-Contrast Images

Devices and methods for determining abnormalities in cells or tissues in a subject from a captured image from the subject are described.

Magnetic particle inspection device

A magnetic particle inspection device includes an image acquisition unit configured to acquire an image obtained by attaching magnetic particles to a magnetized inspection object and capturing the image of the inspection object, and a binarized image obtained by binarizing a whole or a part of the image; a region specifying unit configured to specify a magnetic particle group region based on at least one of the image and the binarized image, the magnetic particle group region containing a magnetic particle group; and a detection unit configured to detect a flaw by processing luminance information and form information with artificial intelligence obtained by supervised learning, the luminance information being obtained by performing statistical processing on luminances of a plurality of minute regions in the specified magnetic particle group region, and the form information being information regarding a form of the magnetic particle group that is obtained from the binarized image.

Video segmentation techniques

A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Video segmentation techniques

A video segmentation system can be utilized to automate segmentation of digital video content. Features corresponding to visual, audio, and/or textual content of the video can be extracted from frames of the video. The extracted features of adjacent frames are compared according to a similarity measure to determine boundaries of a first set of shots or video segments distinguished by abrupt transitions. The first set of shots is analyzed according to certain heuristics to recognize a second set of shots distinguished by gradual transitions. Key frames can be extracted from the first and second set of shots, and the key frames can be used by the video segmentation system to group the first and second set of shots by scene. Additional processing can be performed to associate metadata, such as names of actors or titles of songs, with the detected scenes.

Automating Microfacies Analysis of Petrographic Images

Disclosed herein are methods and systems that determine a microfacies or a microfacies characteristic of a sample of a subterranean formation based on a segmented image of a petrographic image of the sample, wherein the segmented image is derived from the petrographic image using a machine-learning algorithm.

Image classification by brain computer interface

A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Page image display control system, page image display control method, and program therefor

A scene is assigned to each page image, and an image included in the assigned scene is pasted on each page image. A page image addition instruction region is displayed between adjacent page images. In a case where the page image addition instruction region is clicked, a new page image is displayed between two page images between which the clicked page image addition instruction region is interposed. An image included in a scene assigned to a first page image of the two page images or a scene assigned to a second page image of the two page images is pasted on the new page image.

Magnetic resonance image analysis method and method for evaluating the risks of radiotherapy

The present disclosure provides a magnetic resonance (MR) image analysis method for a patient who underwent radiotherapy. The method includes the steps: receiving an MR image set of a patient and a dose map of a radiotherapy plan; converting the dose intensity distribution of the dose map into the relative spatial positions in the MR image set; selecting a radiation dose and a radiation exposure region, wherein the radiation exposure region has radiation intensity being equal to or higher than the radiation dose; using the radiation exposure region to determine a region of interest (ROI) in the MR image set; classifying the voxels inside the ROI of the MR image set into different clusters according to the grayscale values of the voxels inside the ROI; and calculating the volume or ratios of the different clusters inside the ROI. The present disclosure also provides a method for evaluating risks of radiotherapy.

Media enhancement with customized add-on content

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a product video representing a product and review feeds authored by a user. Product features are extracted from the product video and user specific features are extracted from the review feeds. User specific add-on content is generated based on the user specific features and user data. The user specific add-on contents are overlaid onto the product video when the product feature corresponding to a user specific feature of the user specific add-on content.

Item Classification System, Device and Method Therefor

An item classification system for use in an item handling system is disclosed. The classification system comprises processing means configured to: process an image of an item to determine, based on a first model (), one or more predetermined first item types, each first item type defined by one or more first item characteristics; process the image to determine, based on the first model (), a first probability associated with each first item type wherein each first probability is indicative of the likelihood that the item has the first characteristics defining each determined first item type; process the image to determine, based on a second model (), one or more predetermined second item types, each second item type defined by one or more second item characteristics; process the image to determine, based on the second model (), a second probability associated with each second item type wherein each second probability is indicative of the likelihood that the item has the second characteristics defining each second item type; and classify the item according to each first item type and each second item type and the probability associated with each first item type and the probability associated with each second item type. 1. An item classification system for use in an item handling system , the classification system comprising: [{'b': '101', 'i. process an image of an item to determine, based on a first model (), one or more predetermined first item types, each first item type defined by one or more first item characteristics;'}, {'b': '101', 'ii. process the image to determine, based on the first model (), a first probability associated with each first item type wherein each first probability is indicative of the likelihood that the item has the first characteristics defining each determined first item type;'}, {'b': '103', 'iii. process the image to determine, based on a second model (), one or more predetermined second item types, each second item type defined by one ...

METHOD FOR IDENTIFYING A LIP-MAKEUP PRODUCT APPEARING IN AN IMAGE

Disclosed is a method for identifying a lip-makeup product from an image, the method including: detecting a mouth on the image; determining whether the mouth is made-up or not; and if the mouth of the person is made-up: determining color parameters of the lip-makeup of the image from at least some of the pixels of the image belonging to the mouth; computing a distance between the color parameters of the lip-makeup from the image and the color parameters of at least some of a plurality of reference lip-makeup products; and if at least one distance between the color parameters of the lip-makeup of the input image and one of the reference lip-makeup product is below a predetermined threshold, identifying a reference lip-makeup product best matching the lip-makeup product on the image. 1. A method for identifying a lip-makeup product from an input image , the method being implemented by a system comprising a computer and a memory storing , for each of a plurality of reference lip-makeup products , an identifier and color parameters , and comprising:detecting a mouth of a person on the input image,determining whether the mouth is made-up or not, and determining color parameters of the lip-makeup of the input image from at least some of the pixels of the image belonging to the mouth,', 'computing a distance between the color parameters of the lip-makeup from the image and the color parameters of at least some of the plurality of reference lip-makeup products, and', 'if at least one distance between the color parameters of the lip-makeup of the input image and one of the reference lip-makeup product is below a predetermined threshold, identifying a reference lip-makeup product best matching the lip-makeup product on the image., 'if the mouth of the person is made-up,'}2. The lip-makeup identification method according to claim 1 , wherein identifying a reference lip makeup product best matching the lip-makeup product on the image comprises implementing a classification ...

Image Processing Apparatus, Image Processing Method, and a Non-Transitory Recording Medium

Image processing apparatus programmed to: continuously shoot a subject to obtain images, and detect the object and extract a position of the object from a three-dimensional position of the subject in the images; detect the person and extract a position of the person from the three-dimensional position, and extract, from the position of the person, part information pieces including respective positions of characteristic parts of the person; generate a pose class for each set of part information pieces, the part information pieces being similar to one another in correlation between parts of the person calculated from each of the part information pieces; identify a pose class to which the correlation between parts of the person belongs, among generated pose classes, when a distance between the person and the object is within a predetermined range; and store the identified pose class in association with the object.

Sorted geometry with color clustering (sgcc) for point cloud compression

A point cloud compression approach that exploits HEVC for compression of both the geometry and color data is described herein. Although both the geometry and color could be compressed in a lossy fashion, the point cloud compression approach is suggested for the scenario of lossless geometry and lossy color coding. Both geometry and color data are first mapped into 2D images and then compressed by HEVC. A sorting technique is used to sort the geometry data to make it as correlated as possible when it is mapped to a 2D image. For color coding, clustering is used to put similar colors in a point cloud into spatial neighbors in the mapped 2D image. This significantly avoids color leaking due to quantization errors to neighbor points in 3D. The results show that much better compression is achieved compared to the Anchor when it is configured for lossless geometry coding.

System and method for biometric authentication in connection with camera-equipped devices

The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Cart/basket fraud detection processing

An image of a cart/basket is captured based on a triggering event associated with a transaction at a transaction terminal. A determination is made whether the cart/basket is empty or nonempty from the image. When the cart/basket is nonempty a determination is made whether the nonempty cart includes one or more legitimate items or saleable items associated with a store. When the cart/basket is nonempty with one or more saleable items remaining in the cart/basket, and alert is raised to suspend completion of the transaction at the transaction terminal for intervention and audit of the transaction.

CLUSTERED DYNAMIC GRAPH CONVOLUTIONAL NEURAL NETWORK (CNN) FOR BIOMETRIC THREE-DIMENSIONAL (3D) HAND RECOGNITION

A computer-implemented method of characterizing a person's hand geometry includes inputting a three-dimensional (3D) point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN), and processing the 3D point cloud, with a shared network portion of the clustered DGCNN, to create a processed version of the three-dimensional point cloud. The method further includes, with a shape regression network portion of the clustered DGCNN, assigning each respective feature point in the processed version of the 3D point cloud to a corresponding one of a plurality of pre-defined clusters, and applying one or more transformations to the feature points assigned to each respective cluster to produce per cluster shape parameters that represent shapes associated with portions of the person's hand that correspond to associated ones of the pre-defined clusters. Each pre-defined cluster corresponds to a unique part of a hand's surface. 1. A computer-implemented method of characterizing geometry of a person's hand , the method comprising:inputting a three-dimensional point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN);processing the three-dimensional point cloud, with a shared network portion of the clustered DGCNN that comprises one or more convolutional layers, to create a processed version of the three-dimensional point cloud; and ["assigning each respective feature point in the processed version of the three-dimensional point cloud to a corresponding one of a plurality of pre-defined clusters, wherein each pre-defined cluster corresponds to a unique part of a hand's surface; and", "applying one or more transformations to the feature points assigned to each respective cluster to produce per cluster shape parameters that represent shapes associated with portions of the person's hand that correspond to associated ones of the pre-defined clusters."], 'with a shape regression network ...

Method and system for imaging and analysis of anatomical features

A method and system are provided for characterizing a portion of biological tissue. This invention comprises a smartphone and tablet deployable mobile medical application that uses device sensors, internet connectivity and cloud-based image processing to document and analyze physiological characteristics of hand arthritis. The application facilitates image capture and performs image processing that identifies hand fiduciary features and measures hand anatomical features to report and quantify the progress of arthritic disease.

A distance statistics based method for 3d sonar point cloud image enhancement

The present invention discloses a distance statistics based method for 3D sonar point cloud image enhancement, comprising the following steps: (1) obtaining sonar data, and converting 3D sonar range image information corresponding to sonar data per frame into point cloud data in global coordinate; (2) using a kd-tree to search the point cloud data, and calculate Euclidean distance L ij between point P i and the nearest K point cloud data; wherein, value range of i and j is 1≤i≤N and 1≤j≤K respectively; N refers to the total quantity of point cloud data; (3) excluding point cloud data corresponding to mean value of L ij which do not match the certain Gaussian distribution, and complete enhancement of 3D sonar point cloud image. Such method features in easy operation, high efficiency and convenience, which can effectively remove outlier points to minimize noise, and enhance point cloud image.

Prediction on top-down scenes based on object motion

Techniques for determining predictions on a top-down representation of an environment based on object movement are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) may capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle, a pedestrian, a bicycle). A multi-channel image representing a top-down view of the object(s) and the environment may be generated based in part on the sensor data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) may also be encoded in the image. Multiple images may be generated representing the environment over time and input into a prediction system configured to output a trajectory template (e.g., general intent for future movement) and a predicted trajectory (e.g., more accurate predicted movement) associated with each object. The prediction system may include a machine learned model configured to output the trajectory template(s) and the predicted trajector(ies).

Image filtering for subject and crowd identification

Systems and methods disclosed herein for providing people categorization within electronic images. One embodiment involves retrieving an input image from memory. The embodiment further involves determining one or more facial regions in the input image using a facial detection algorithm. The embodiment further involves identifying a number of features for each facial region. The embodiment further involves determining a subject face and a crowd face in the input image based on at least the number of features for the subject face being more than the number of features for the crowd face. The embodiment further involves displaying, on a display device, the subject face identified within the image.

Efficient artificial intelligence analysis of radiographic images with combined predictive modeling

A system, an image analyzer and a method for diagnosing a presence of a disease or a condition in an image of a subject, for example, a veterinary patient, are provided including: classifying the image to a body region, and obtaining a classified, labeled, cropped, and oriented sub-image; directing the sub-image to artificial intelligence processor for obtaining an evaluation result, and comparing the evaluation result to a database library of evaluation results and matched written templates or a dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report to display the report to a radiologist. These system, analyzer and method are achieved in greatly reduced lengths of time and are useful for cost and time savings.

Method of selecting an article for covering a body part by processing the image of the body part

A method of measuring the distance of two points of an object by processing the image of the object taken from the mobile phone. The image taken by the mobile phone should contain the object and a piece of a rectangular paper marked with a point on each corner. For example, the object (e.g., a human foot) stands on a piece of the rectangular paper which has a mark on each corner. The image taken by a mobile phone should contain the foot and at least three of the four marks. Likewise, a human standing against the wall aside with the marked paper and a hand pressing on the marked paper are all applicable to the method of measuring any two points in the image of the. The resulted distance of any two points can be used to obtain a correct size of foot or the like.

Methods, materials and apparatus for mobile additive manufacturing of advanced structures and roadways

The present disclosure provides various aspects for mobile and automated processing utilizing additive manufacturing and the methods for their utilization. In some examples, discrete material formats for use in an Additive Manufacturing Array are disclosed. Methods of using the additive manufacturing robot, discrete materials, and the roadways produced with the additive manufacturing robot are provided. A combined function Addibot, with Additive Manufacturing capabilities, cleaning capabilities, line painting capabilities and seal coating capabilities which may be used in concert with a camera equipped aerial drone for design and characterization function is described.

Machine continuous learning method of neural network object classifier and related monitoring camera apparatus

A machine continuous learning method with a neural network object classifying function is applied to a monitoring camera apparatus having a processor with an object classifier. The machine continuous learning method includes utilizing the processor to receive an image, utilizing the object classifier to analyze the image for generating a first parameter and a second parameter, utilizing the processor to determine whether the first parameter belongs to at least one cluster established by human feedback, and utilizing the processor to output a label of the at least one cluster or the second parameter generated by the object classifier according to a determination result.

Group and conversational framing for speaker tracking in a video conference system

In one embodiment, a method is provided to intelligently frame groups of participants in a meeting. This gives a more pleasing experience with fewer switches, better contextual understanding, and more natural framing, as would be seen in a video production made by a human director. Furthermore, in accordance with another embodiment, conversational framing techniques are provided. During speaker tracking, when two local participants are addressing each other, a method is provided to show a close-up framing showing both participants. By evaluating the direction participants are looking and a speaker history, it is determined if there is a local discussion going on, and an appropriate framing is selected to give far-end participants the most contextually rich experience.

Lane count estimation

A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Image area extraction method and storage program

Provided is an image area extraction method for extracting an image area of an object from a color image of obtained color image data. The image data extraction method includes converting RGB values of each pixel in the color image data into HSV values, performing threshold processing to binarize at least one of the converted S and V values of each pixel so that it will be converted into HS′V′ values, generating composite image data including an X value, a Y value, and a Z value for each pixel, the X value, the Y value, and the Z value being obtained by adding values according to predetermined one-to-one combinations between any one of an R value, a G value, and a B value and any one of an H value, an S′ value, and a V′ value, and extracting the image area using the composite image data.

Feature and Case Importance and Confidence for Imputation in Computer-Based Reasoning Systems

Techniques are provided for imputation in computer-based reasoning systems. The techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute (e.g., missing fields) in the computer-based reasoning model and determining conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining for which cases to impute data and, for each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data. Control of a system is then caused using the updated computer-based reasoning model.

Model training method and apparatus, storage medium, and device

A model training method and apparatus, a storage medium, and a device, relating to the field of artificial intelligence (AI) technologies are provided. The method includes: obtaining a plurality of training samples, each training sample including an interaction screen and an action label, the action label indicating an interaction action adopted by a character object in the interaction screen; extracting features from the interaction screens included in the plurality of training samples, and performing clustering according to the extracted features, to obtain a clustering result; determining at least one key sample from the plurality of training samples according to the clustering result; and setting a weight for each training sample, and updating a network parameter of a deep network based on the plurality of training samples with the weights, the weight of each key sample being greater than the weight of another sample in the plurality of training samples.

Method and device for generating painting display sequence, and computer storage medium

A method and device for generating a painting display sequence, and a computer storage medium are provided. The method for generating a painting display sequence comprises the following steps: acquiring painting data of a region of interest (ROI); clustering the painting data in a predetermined group and obtaining clustering results; and generating the painting display sequence according to the clustering result.

Object detection improvement based on autonomously selected training samples

A method for generating positive and negative training samples is presented. The method includes identifying false positive images of an object based on multiple images of an environment. The method also includes generating positive training samples from a set of images of the object. The method further includes generating a negative training sample from the false positive image. The method still further includes training an object detection system based on the positive training samples and the negative training sample.

System and Method for Biometric Authentication in Connection with Camera Equipped Devices

The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Cluster based photo navigation

The technology relates to navigating imagery that is organized into clusters based on common patterns exhibited when imagery is captured. For example, a set of captured images which satisfy a predetermined pattern may be determined. The images in the set of set of captured images may be grouped into one or more clusters according to the predetermined pattern. A request to display a first cluster of the one or more clusters may be received and, in response, a first captured image from the requested first cluster may be selected. The selected first captured image may then be displayed.

Method and system for image segmentation using a directed graph

Method and system is disclosed for image segmentation. The method includes acquiring a digital image, constructing a directed graph from the digital image, calculating a plurality of cost functions, constructing an electrical network based upon the constructed directed graph and the plurality of calculated cost functions, simulating the electrical network using fixed-point linearization, and thresholding the voltages in the simulated electrical network to produce image segmentation. Simulation may be executed in parallel to achieve desirable computational efficiencies.

Method and apparatus for organizing media content

A method that incorporates teachings of the subject disclosure may include, for example, determining, by a system comprising a processor, more common features of a plurality of images according to similarity matrices indicating relative similarities between instances of common features occurring within multiple images of the plurality of images, defining, by the system, cluster groups associated with the more common features, where each cluster group comprises cluster images of the plurality of images, and where the more common features are present in each the cluster images, and performing, by the system, quality-based filtering on the cluster images to identify a target cluster image to represent the cluster images for each of the cluster groups. Other embodiments are disclosed.

Configuring spanning elements of a signature generator

Systems, and method and computer readable media that store instructions for configuring spanning elements of a signature generator.

Multi-scale pyramid arrangement for use in an image segregation

Multiple sets include multiple scale-spaced pyramids representing each of the image and selected characteristics of the image relevant to the identification of the illumination and material aspects of the image.

Image processing device, image processing method and image processing program

An image processing device includes an image acquisition means that acquires a target image, which is an image to be processed, an extraction means that extracts a plurality of partial regions from the target image by clustering based on specified color similarity of pixel values, a generation means that generates a plurality of composite images, each of which is composed of one or more partial regions out of the plurality of partial regions, a calculation means that calculates, for each of the composite images, a score indicating a likelihood that a shape formed by the partial region constituting the composite image is a shape of an object to be extracted, and an output means that outputs processing target region information specifying a composite image with the highest score as an object region where the object is shown in the target image.

Configuring spanning elements of a signature generator

Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Method and apparatus for generating, updating classifier, detecting objects and image processing device

The present invention relates to method and apparatus for generating, updating classifier, detecting objects, and image processing device. A method for generating a multi-class classifier, comprising the following steps: generating at least one one-class background classifier by using a one-class object classifier and background image regions obtained from a sequence of images; and assembling the one-class object classifier and the at least one one-class background classifier into a multi-class classifier.

Identifying living skin tissue in a video sequence

According to an aspect, there is provided an apparatus for identifying living skin tissue in a video sequence, the apparatus comprising a processing unit configured to receive a video sequence, the video sequence comprising a plurality of image frames; divide each of the image frames into a plurality of frame segments, wherein each frame segment is a group of neighboring pixels in the image frame; form a plurality of video sub-sequences, each video sub-sequence comprising a frame segment from two or more of the plurality of image frames; analyze the plurality of video sub-sequences to determine a pulse signal for each video sub-sequence; determine a similarity matrix based on pairwise similarities for each determined pulse signal with each of the other determined pulse signals; and identify areas of living skin tissue in the video sequence from the similarity matrix.

Image transmission system, image processing apparatus, image storage apparatus, and control methods thereof

A transmission device divides image data into a plurality of regions on the basis of a similarity of pixels and transmits the image data and region representative points of the plurality of regions. A reception device receives the image data and the region representative points transmitted from the transmission device. The reception device generates region labels used for identifying the plurality of regions using the image data and the region representative points.

Sensor location determination for geographical region

A method can include generating a feature vector based on image data and soil data, the image data of an image of the geographical region produced by an aerial vehicle, the image data representative of an attribute of the attributes of the soil or foliage, and the soil data indicating physical characteristics of soil of cells within the geographical region, producing a matrix including entries indicating how similar the cells are in terms of the image data and the soil data based on the feature vector, and producing based on the matrix, data indicating a cluster of clusters to which each cell of the cells belongs, each cell more similar to other cells of the cluster to which they belong than cells of other clusters, each cluster indicating a location at which to situate a sensor of the sensors to monitor the attribute.

Image recognition device and method for registering feature data in image recognition device

An image recognition device has a database in which pieces of feature data of a plurality of objects are registered while divided into classes for each of the plurality of objects; an identification unit that identifies an unknown object by evaluating which feature data of the class registered in the database is most similar to feature data obtained from an image of the unknown object, and a feature data registration unit that registers feature data in the database. The database is capable of setting a plurality of classes to an identical object. The feature data registration unit, in adding new feature data with respect to a first object already registered in the database, sets a new class other than an existing class with respect to the first object.

Vanishing point computation for single vanishing point images

Certain embodiments involve calculating a vanishing point location of an image to realistically blend multiple images. For example, a method for modifying image content based on a vanishing point location computed for a background image includes receiving the background image and classifying a set of planes in the background image. The method also includes identifying, using plane boundaries, a first set of line segments that define first convergence points. Additionally, the method includes identifying a second set of line segments that are positioned within individual planes and that define second convergence points. Further, the method includes grouping the first convergence points and the second convergence points into a cluster and computing the vanishing point location from an average of point locations in the cluster. Furthermore, the method includes manipulating a feature image overlaid on the background image to generate a blended image based on the vanishing point location.

Method and apparatus for recognizing object based on vocabulary tree

A method and apparatus for recognizing an object may obtain, from an input image, feature points and descriptors corresponding to the feature points, determine indices of the feature points based on the descriptors, estimate a density distribution of feature points for each of the indices, and recognize an object included in the input image based on the estimated density distribution.

Material classification using brdf feature vector

Feature vector representations are computed for BRDF image slices in a database of known materials captured under a relatively large number of incident illumination directions. Low-level features of each image slice are clustered into at least two clusters. An intermediate feature vector representation is computed for each image slice with entries that are weighted means of the clusters.

Anomalous object interaction detection and reporting

Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.

Method and apparatus for defining a storyline based on path probabilities

A method, apparatus and computer-readable storage medium are provided to define a storyline based on path probabilities for a plurality of paths through the frames of a video. Relative to a method and for a plurality of frames of a video, regions of a first frame and regions of a second, subsequent frame that have been viewed are identified. tified. For each of at least one first-frame region of one or more regions of the first frame, the method determines a transition probability of transitioning from a respective first-frame frame region of the first frame to each of at least one second-frame region of a plurality of regions of the second frame. Based on the transition probabilities, the method determines a path probability for each of at least one of a plurality of paths through the frames of the video. The method additionally defines a storyline based on the path probabilities.

Digital picture frame photograph clustering

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.

Accelerated training of an image classifier

Systems and methods are provided for generating labeled image data for improved training of an image classifier, such as a multi-layered machine learning model configured to identify target image objects in image data. When the initially trained classifier is unable to identify a particular object in input image data, such as an object that did not appear in initial training data, feature information determined by the classifier for the given image data may be provided to a clustering model. The clustering model may group image data having similar features into different clusters or groups, which may in turn be labeled at the group level by an annotator. The image data assigned to the different clusters, along with the associated labels, may subsequently be used as training data for training a classifier to identify the labeled objects in images.

Image segmentation confidence determination

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND PROGRAM

An object detection device () includes an object detection unit () that detects an object from an image including the object by neural computation using a CNN. The object detection unit () includes: a feature amount extraction unit () that extracts a feature amount of the object from the image; an information acquisition unit () that obtains a plurality of object rectangles indicating candidates for the position of the object on the basis of the feature amount and obtains information and a certainty factor of a category of the object for each of the object rectangles; and an object tag calculation unit () that calculates, for each of the object rectangles, an object tag indicating which object in the image the object rectangle is linked to, on the basis of the feature amount. The object detection device () further includes an excess rectangle suppression unit () that separates a plurality of object rectangles for which a category of the object is the same into a plurality of groups according to the object tags, and deletes an excess object rectangle in each of the separated groups on the basis of the certainty factor. 1. An object detection device comprising a hardware processor that detects an object from an image including the object by neural computation using a convolutional neural network , whereinthe hardware processor:extracts a feature amount of the object from the image;obtains a plurality of object rectangles indicating candidates for a position of the object on the basis of the feature amount and obtains information and a certainty factor of a category of the object for each of the object rectangles; andcalculates, for each of the object rectangles, an object tag indicating which object in the image the object rectangle is linked to, on the basis of the feature amount, andthe hardware processor further separates the plurality of object rectangles for which the category of the object is the same into a plurality of groups according to the object tags, and ...

SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO PROVIDE IMAGE-BASED CELL GROUP TARGETING

Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

Grouped Mathematical Differentiable NMS For Object Detection

An end-to-end trainable grouped mathematically differentiable non-maximal suppression (NMS) technique is presented for monocular 3D object detection. First, formulate NMS as a matrix operation and then group and mask the boxes in an unsupervised manner to obtain a simple closed-form expression of the NMS. This technique addresses the mismatch between training and inference pipelines and, therefore, forces the network to select the best 3D box in a differentiable manner. As a result, the proposed technique achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset performing comparably to monocular video-based methods. 1. A computer-implemented method for detecting objects in an image , comprising:receiving, by a computer processor, a set of predicted bounding boxes from at least one of a two-dimensional classification head or a three-dimensional classification head;receiving, by the computer processor, scores for each of the predicted bounding boxes in the set of predicted bounding boxes, where the scores for each of the predicted bounding boxes are in form of a vector;computing, by the computer processor, a set of intersection over union (IoU) measures for the set of predicted bounding boxes;grouping, by the computer processor, boxes in the set of predicted bounding boxes into one or more groups of predicted bounding boxes;for each group in the one or more groups of predicted bounding boxes, calculating, by the computer processor, rescores for each of the predicted bounding boxes in the set of predicted bounding boxes by performing matrix operations on the vector of scores in accordance with a closed-form expression; andselecting a subset of boxes from the set of predicted bounding boxes for each object in the image using the rescores for each of the predicted bounding boxes.2. The method of further comprisescapturing image data for a scene using an imaging device; anddetermining the set of predicted bounding boxes from the image ...

一种基于细胞散点图的异常检测方法

本发明公开了一种基于细胞散点图的异常检测方法，包括以下步骤：S1、获取样本的细胞散点图，在细胞散点图内建立平面坐标系，并遍历每个点，将坐标信息和种类信息赋予每个细胞点；S2、根据细胞种类信息，计算各种细胞的细胞簇中心和离群容忍度；S3、根据各细胞种类的细胞簇中心和离群容忍度进行高斯分布建模；S4、设定风险数据阈值，对每种细胞种类的容忍度外各点进行风险数据评分；S5、根据风险数据评分判断各种类细胞是否存在。本发明避免给机器的便携性和成本加大了负担，不增加额外的损耗。

Image photographing apparatus and control method thereof

영상 촬영 장치 및 이의 제어 방법이 제공된다. 본 영상 촬영 장치의 제어 방법은 사용자의 촬영 명령이 입력되면, 기설정된 시간 내에 연속 촬영을 수행하여 초점 거리가 동일한 복수의 영상을 획득하고, 복수의 영상을 이용하여 모션 벡터를 산출하고, 모션 벡터 및 복수의 영상 중 제1 영상의 색 정보를 바탕으로 제1 영상의 전경 및 후경을 분리하며, 분리된 전경 및 후경을 바탕으로 아웃 포커싱을 수행한다. An image photographing apparatus and a method for controlling the same are provided. In this method of controlling an image photographing apparatus, when a user's photographing command is input, continuous photographing is performed within a preset time to acquire a plurality of images having the same focal length, a motion vector is calculated using the plurality of images, and a motion vector and separating a foreground and a rear view of the first image based on color information of the first image among the plurality of images, and performing out-focusing based on the separated foreground and rear view.

Data access interface

The system, method, and computer readable medium described herein provide improvements in the ways that user interfaces present multi-dimensional relationships between data samples to a user. The disclosed user interface framework provides users with a visualization of the complex relationships between data samples having multi-dimensional attributes which allows the users to quickly and intuitively grasp the relationships between data samples for a large number of attributes at a glance and in a single data map visualization.

Packaging machine defect product detection method based on machine vision

本发明涉及图像处理技术领域，具体涉及一种基于机器视觉的包装机缺陷产品检测方法，该方法获取模板图像和待检测图像；通过分区步骤将模板图像划分为多个区域，并获取待检测区域对应的多个区域；对于待检测图像和模板图像，利用文字边缘点的角点值对比文字区域的第一差异性，基于纹理特征对比待图像区域的第二差异性，根据边缘像素点的角点值对比边缘区域的第三差异性，通过灰度作差对比其他区域的第四差异性；进而获取待检测图像与模板图像的整体匹配度，当整体匹配度低于预设阈值时，对应的包装袋为残次品。本发明提高了与模板图像进行匹配时的精度，并且能够在不增加机械复杂度的前提下，检测多种缺陷。

Removing moving objects from a video scene captured by a moving camera

Methods, an apparatus, and software media are provided for removing unwanted information such as moving or temporary foreground objects from a video sequence. The method performs, for each pixel, a statistical analysis to create a background data model whose color values can be used to detect and remove the unwanted information. The method assumes that for each pixel the background is present in a majority of the frames. The camera that records the video sequence may move relative to the geometry of the video scene. A pixel in a first frame is matched to a location in the geometry. The method determines color values of pixels, matched to the location in the geometry, in successive frames and clusters color values to determine a background color value range. It may use quadratic or better interpolation and extrapolation to determine background color values for unavailable frames.

Method for detecting wafer low-texture defects

本发明涉及图像处理领域，具体涉及一种晶圆低纹理缺陷的检测方法，基于图像处理，采集待检测晶圆表面灰度图像，划分为大小相同的区域，根据每个区域像素点的灰度值均值和灰度值方差得到每个区域和其他区域的结构相似性，根据结构相似性和每个区域像素点的灰度累加值得到每个区域和相邻区域之间的整体相似性得到整体相似性矩阵，对矩阵中的整体相似性数据进行密度聚类，根据聚类结果判断晶圆是否存在异常，对存在异常的晶圆获取到异常整体相似性数据对应的区域，根据该区域中晶粒和标准晶粒灰度值构建灰度差异矩阵，根据灰度差异矩阵计算每个晶粒的异常程度，根据异常程度判断晶粒是否异常，方法智能、精准、高效。

Apparatus and method for processing texture image

본 발명은 텍스처 이미지 처리 장치 및 방법에 관한 것이다. 본 발명에 따른 장치는, 입력 이미지의 에지를 검출하고, 검출된 에지 정보를 이용해 입력 이미지를 에지(edge)의 밀도를 표현하는 이미지로 변환하는 필터부, 상기 변환된 이미지에 포함된 노이즈를 제거하고 스무딩 처리하는 스무딩 처리부, 상기 스무딩 처리된 이미지를 미리 정해진 횟수만큼 이미지 분할 개수를 변경하여 클러스터링하는 클러스터링부, 및 상기 입력 이미지에 대한 클러스터 개수를 미리 학습된 검증 데이터를 기준으로 최적화하여 상기 입력 이미지에 대응하는 최종 클러스터 개수를 결정하고, 서로 다른 개수로 분할된 클러스터링 결과 중 상기 최종 클러스터 개수에 대응하는 결과 이미지를 선택하여 출력하는 클러스터 최적화부를 포함한다. The present invention relates to a texture image processing apparatus and method. The apparatus according to the present invention includes a filter unit that detects an edge of an input image and converts the input image into an image expressing the density of edges by using the detected edge information, and removes noise included in the converted image and a smoothing processing unit for smoothing, a clustering unit for clustering the smoothed image by changing the number of image divisions a predetermined number of times, and optimizing the number of clusters for the input image based on pre-learned verification data to optimize the input image and a cluster optimizer for determining the final number of clusters corresponding to , and selecting and outputting a result image corresponding to the final number of clusters from among clustering results divided into different numbers.

Tracking method

본원 발명은 프레임의 시퀀스 내에서 하나 이상의 객체를 트랙킹하기 위한 방법에 관한 것으로서, 각각의 상기 프레임은 픽셀 어레이를 포함하며, 상기 각 픽셀에 대해서 심도 값이 연관된다. 상기 방법은 각 프레임의 상기 픽셀들의 적어도 일부를 몇개의 영역으로 그룹핑하는 단계; 상기 영역들을 상호연결된 영역들의 클러스터들(B 1 , ..., B 5 )로 그룹핑하는 단계; 및 만약 하나 이상의 클러스터(B 2 , ..., B 5 )가 다른 클러스터(B 1 )의 심도 값 보다 더 높은 심도 값을 가진다면, 2차원 프로젝션의 다른 클러스터(B 1 )에 인접한 하나 이상의 클러스터(B 2 , ..., B 5 )가 상기 다른 클러스터(B 1 )에 의해서 부분적으로 은폐된 객체에 속하는 것으로 결정하는 단계를 포함한다.

Multiple-wavelength images analysis electro optical system for detection of accident ship and submerged person and analysis method thereof

A multi-wavelength image analysis electro-optical system for detecting accident ships and drowned people according to the present invention is a multi-wavelength image analysis electro-optical system for detecting accident ships and drowned people, the system comprising an input unit (10) comprising a super-low illumination camera (3), a short-wavelength infrared image sensor (5), a medium-wavelength infrared image sensor (7), and a long-wavelength infrared image sensor (9); a signal processing unit (20) which receives and processes data of the input unit (10); a display unit (30) which receives and displays data of the signal processing unit (30); a storage unit (40) which stores data of the signal processing unit (20) and the display unit (30); and a control unit (90) which has a camera control board (50) and a driving control board (60) and controls the input unit (10), the signal processing unit (20), the display unit (30), and the storage unit (40).

Lane line processing method and device

本发明实施例提出一种车道线处理方法和装置。该方法包括：对第一图像进行二值化处理，得到二值图像，所述第一图像中包括车道线点和非车道线点；对所述二值图像进行连通域分析，得到所述二值图像中的一个或多个连通域，每个连通域中包括相互邻接的多个车道线点；利用各所述连通域确定各车道线对应的分组中包括的车道线点；利用各所述分组中包括的车道线点得到各所述分组对应的车道线的表示信息。本发明实施例，利用连通域分析方法对二值图像上的车道线点进行分组，得到的分组准确，不受图像质量的影响，具有较高的鲁棒性。

Method and apparatus for determining liveness and method and appartus for learing thereof

라이브니스 판단 장치 및 방법과 라이브니스 판단 장치의 학습 장치 및 방법이 개시된다. 라이브니스 판단 방법은 프로세서에 의해, 입력 지문 영상으로부터 특징을 추출하고, 현재 계층 분류기에 상기 특징을 입력하고, 현재 계층 분류기의 출력이 라이브(Live)인 경우, 다음 계층 분류기에 상기 특징을 입력하고, 다음 계층 분류기의 출력이 위조(Fake)인 경우, 입력 지문 영상의 라이브니스를 거짓(False)인 것으로 판단한다. 여기서, 현재 계층 분류기 및 다음 계층 분류기는 각각 서로 다른 그룹에 속하는 복수의 학습 위조 영상을 기초로 학습된 것이다.

Image topological coding for visual search

A method and an apparatus for processing an image generate a first vector of a first number dimension for the image from a first number of points of the image based on topological information of the first number of points, and the first vector for the image is invariant to rotation and scaling in creating the image. The first number of points may be locations of a set of rotation and scaling invariant feature points for the image, and the generated first vector may be a graph spectrum of a pair-wise distance matrix generated from the first number of points of the image.

Real-time monitoring method for steel wire rope core conveying belt

本发明数据数据处理技术领域，具体涉及一种钢丝绳芯输送带实时监控方法。包括：根据各初始类别组中的两个类别对应的聚类中心之间的距离以及各初始类别组中的两个初始类别对应的特征方差和特征均值，得到各初始类别组对应的影响权重；根据各初始类别组对应的影响权重，对各初始类别组进行分类，得到各特征类别以及各特征类别中的各初始类别组；根据初始窗口的尺寸、聚类效果评估值以及特征占比，得到目标窗口；根据目标窗口，利用均值漂移聚类对所述当前帧X光图像上的各像素点进行再聚类，得到各目标类别，将各目标类别中的各像素点的目标亮度值记为对应目标类别的聚类中心的亮度值。本发明能可靠对钢丝绳芯输送带进行实时监控。

METHOD AND SYSTEM FOR RECOGNIZING OBJECTS REPRESENTED IN AN IMAGE BY A CLOUD OF POINTS

Ein Verfahren zum Erkennen von einem oder mehreren in einem Bild anhand einer M-dimensionalen Punktwolke, mit M>1, aus einer Mehrzahl n von Punkten repräsentierten Objekten, weist auf: Bestimmen, für jede einer Anzahl m, mit m > 0, von bestimmten eindimensionalen Größen, eines jeweiligen zugeordneten Werts der Größe zu jedem der Punkte auf Basis von dessen Position oder Eigenschaften; Bestimmen, für jede der Größen, einer jeweiligen Häufigkeitsverteilung bezüglich der für die verschiedenen Punkte jeweils bestimmten Werte dieser Größe; Approximieren jeder der Häufigkeitsverteilungen mittels einer jeweiligen Linearkombination einer endlichen Anzahl von der zugrundeliegenden Größe zugeordneten eindimensionalen Wahrscheinlichkeitsdichtefunktionen; Segmentieren des Bildes so, dass im Fall m=1 jeder der Wahrscheinlichkeitsdichtefunktionen und im Fall m>1 jedem Produkt aus m Wahrscheinlichkeitsdichtefunktionen, wobei je eine der zugeordneten Wahrscheinlichkeitsdichtefunktionen je Größe in dem Produkt vertreten ist, ein jeweiliges Segment des Bildes eindeutig zugeordnet wird; Jeweiliges Zuordnen jedes Punkts der Punktwolke zu demjenigen Segment, dessen zugeordnete Wahrscheinlichkeitsdichtefunktion im Fall m=1 bzw. dessen zugeordnetes Produkt im Fall m>1 an der Stelle, die durch die dem Punkt zugeordneten Werte der m Größen bestimmt ist, den relativ größten Funktionswert bzw. Produktwert unter den Wahrscheinlichkeitsdichtefunktionen bzw. Produkten aufweist; und Identifizieren zumindest eines derjenigen Segmente, denen jeweils zumindest eine vorbestimmte Mindestanzahl von Punkten zugeordnet wurde, als Repräsentant eines jeweiligen erkannten Objekts. Eine entsprechende Vorrichtung und ein Computerprogramm sind eingerichtet, das Verfahren auszuführen. A method for recognizing one or more objects in an image based on an M-dimensional point cloud, with M>1, represented by a plurality n of points, comprises: determining, for each of a number m, with m>0, certain ...

Retrieving fields using neural networks without using templates

FIELD: information technology. SUBSTANCE: invention relates to the field of information technologies, specifically to text recognition systems. Method of analysing unstructured documents presented in a digital format, comprising: obtaining a document dummy comprising a plurality of fields; identification of document, determination of multiple sequences of symbols in document, processing by means of processor device of multiple sequences of symbols in document using first neural network associated with first type of documents, to determine relationship between first field of plurality of fields with first sequence of symbols from plurality of sequences of symbols in document, and wherein said first neural network comprises one or more neural subnets containing at least one hidden layer of neurons. EFFECT: technical result is aimed at improvement of accuracy of field recognition in unstructured documents. 19 cl, 16 dwg РОССИЙСКАЯ ФЕДЕРАЦИЯ (19) RU (11) (13) 2 737 720 C1 (51) МПК G06K 9/66 (2006.01) G06N 3/02 (2006.01) ФЕДЕРАЛЬНАЯ СЛУЖБА ПО ИНТЕЛЛЕКТУАЛЬНОЙ СОБСТВЕННОСТИ (12) ОПИСАНИЕ ИЗОБРЕТЕНИЯ К ПАТЕНТУ (52) СПК G06K 9/66 (2020.05); G06N 3/02 (2020.05) (21)(22) Заявка: 2019137304, 20.11.2019 (24) Дата начала отсчета срока действия патента: (73) Патентообладатель(и): Общество с ограниченной ответственностью "Аби Продакшн" (RU) Дата регистрации: 02.12.2020 (45) Опубликовано: 02.12.2020 Бюл. № 34 2 7 3 7 7 2 0 R U (54) ИЗВЛЕЧЕНИЕ ПОЛЕЙ С ПОМОЩЬЮ НЕЙРОННЫХ СЕТЕЙ БЕЗ ИСПОЛЬЗОВАНИЯ ШАБЛОНОВ (57) Реферат: Изобретение относится к области обработку с помощью процессорного устройства информационных технологий, а именно к множества последовательностей символов в системам распознавания текста. Технический документе с применением первой нейронной сети, результат направлен на повышение точности ассоциированной с первым типом документов, распознавания полей в неструктурированных для определения взаимосвязи между первым документах. Способ анализа полем из множества полей с первой ...

DEVICE AND METHOD FOR RECOGNITION OF GESTURES ON THE BASIS OF ANALYSIS OF MANY POSSIBLE SECTION BORDERS

1. Способ, содержащий этапы, на которых:идентифицируют множество возможных границ раздела в изображении;получают соответствующие модифицированные изображения для соответственных возможных границ раздела;применяют функцию отображения к каждому из модифицированных изображений, чтобы сгенерировать соответствующий вектор;определяют наборы оценок для соответственных векторов по отношению к намеченным параметрам класса; ивыбирают конкретную одну из возможных границ раздела на основе наборов оценок;при этом упомянутые этапы идентификации, получения, применения, определения и выбора реализуются в, по меньшей мере, на одном устройстве обработки, содержащем процессор, соединенный с памятью.2. Способ по п.1, в котором при идентификации множества возможных границ раздела идентифицируют множество возможных границ раздела ладони, ассоциированных с рукой на изображении.3. Способ по п.1, дополнительно содержащий этапы, на которых:принимают входное изображение; ивыполняют одну или более операций нормализации над входным изображением для получения нормализованного изображения, в котором идентифицированы возможные границы раздела.4. Способ по п.3, в котором упомянутые одна или более операций нормализации содержат, по меньшей мере, одну из нормализации ориентации и нормализации масштаба.5. Способ по п.4, в котором нормализация ориентации содержит:определение основного направления руки в пределах входного изображения; иповорот входного изображения на величину, основанную на упомянутом определенном основном направлении руки.6. Способ по п.1, дополнительно содержащий этап, на котором выбирают конкретный один из множест� РОССИЙСКАЯ ФЕДЕРАЦИЯ (19) RU (11) (51) МПК G06F 17/00 (13) 2013 134 325 A (2006.01) ФЕДЕРАЛЬНАЯ СЛУЖБА ПО ИНТЕЛЛЕКТУАЛЬНОЙ СОБСТВЕННОСТИ (12) ЗАЯВКА НА ИЗОБРЕТЕНИЕ (21)(22) Заявка: 2013134325/08, 22.07.2013 (71) Заявитель(и): ЭлЭсАй Корпорейшн (US) Приоритет(ы): (22) Дата подачи заявки: 22.07.2013 (43) Дата публикации заявки: 27.01.2015 Бюл. № 3 Стр.: 1 A 2 0 1 3 1 3 4 3 2 ...

Grader generates, updates and method for checking object and device and image processing equipment

本公开涉及分类器生成、更新与对象检测方法和装置及图像处理设备。一种多类分类器生成方法，包括以下步骤：通过使用一个单类对象分类器和从图像序列中获取的背景图像区域，生成至少一个单类背景分类器；以及将所述单类对象分类器与所述至少一个单类背景分类器组合为一个多类分类器。

Ocean buoy production quality detection method

本发明涉及图像数据处理技术领域，具体涉及一种海洋浮标生产质量检测方法。该方法采集海洋浮标图像和对应的浮标边缘轮廓；比较海洋浮标图像和浮标模板图像的浮标边缘轮廓的差异得到轮廓相似度；根据海洋浮标图像的浮标边缘轮廓上的采样点邻域内的不同类别像素点之间的灰度差异得到海洋浮标图像的整体对比度；基于不同距离下得到的海洋浮标图像的整体对比度的变化，获取对比度衰减程度；基于轮廓相似度、整体对比度和对比度衰减程度得到浮标质量评价指标。本发明通过对海洋浮标图像的表面特征进行分析得到轮廓相似度，同时根据不同距离下浮标的整体对比度，计算出浮标的质量指标，实现了根据浮标的对比突出程度检测浮标的质量的目的。

Methods, systems and apparatus for segmenting and dimensioning objects

Methods, systems, and apparatus for segmenting and dimensioning objects are disclosed. An example method disclosed herein includes determining a first sensor of a plurality of sensors toward which a vehicle is moving based on image data generating by the plurality of sensors; designating the first sensor as a reference sensor; combining the image data from the plurality of sensors to generate combined image data representative of the vehicle and an object carried by the vehicle, the combining based on reference sensor; generating a plurality of clusters based on the combined image data; and identifying a first one of the clusters nearest the reference sensor as the object.

Unsupervised image segmentation method based on Chan-Vese model

本发明提出一种基于Chan‑Vese模型的无监督图像分割方法，属于计算机应用技术领域及精准医疗领域。本发明首先对原始图像进行裁剪，并对裁剪图像进行超像素分割，然后，根据超像素分割结果建立无向图，将Chan‑Vese模型的能量项用边的权重表示，通过使用马尔科夫链，可以在边的赋值过程中同时考虑超像素之间的距离和灰度的关系。最后通过重复使用最大流分割以及更新边的权重可以得到相应的分割结果。本发明可在缺少标注数据的医学领域里对图像进行自动的精准的分割，从而为后续的医学研究提供分割物体的准确信息。本发明能够在更短的时间以及更少的人工干预下准确地分割出所需目标。

Motion learning device, skill discrimination device, and skill discrimination system