Abstract: The image signature extraction device includes an extraction unit and a generation unit. The extraction unit extracts region features from respective sub-regions in an image in accordance with a plurality of pairs of sub-regions in the image, the pairs of sub-regions including at least one pair of sub-regions in which both a combination of shapes of two sub-regions of the pair and a relative position between the two sub-regions of the pair differ from those of at least one of other pairs of sub-regions, and being classified into a plurality of types based on a combination of shapes of two sub-regions and a relative position between the two sub-regions of each of the pairs. The generation unit generates an image signature to be used for identifying the image based on the extracted region features of the respective sub-regions.

Abstract: According to one embodiment, a facial image search system including attribute discrimination module configured to discriminate attribute based on facial feature extracted, a plurality of search modules configured to store facial feature as database in advance, add facial feature extracted to database, calculate degree of similarity between facial feature extracted and facial feature contained in database, setting module configured to generate setting information by associating with any attribute with information indicating search module, and control module configured to identify one or a plurality of search modules based on setting information and attribute and transmit facial feature extracted by feature extraction module to identified search modules.

Abstract: A method for converting a 2D image into a 3D image includes receiving the 2D image; determining whether the received 2D image is a portrait, wherein the portrait can be a face portrait or a non-face portrait; if the received 2D image is determined to be a portrait, creating a disparity between a left eye image and a right eye image based on a local gradient and a spatial location; generating the 3D image based on the created disparity; and outputting the generated 3D image.

Abstract: Image labeling with global parameters is described. In an embodiment a pose estimation system executes automatic body part labeling. For example, the system may compute joint recognition or body part segmentation for a gaming application. In another example, the system may compute organ labels for a medical imaging application. In an example, at least one global parameter, for example body height is computed for each of the images to be labeled. In an example, the global parameter is used to modify an image labeling process. For example the global parameter may be used to modify the input image to a canonical scale. In another example, the global parameter may be used to adaptively modify previously stored parameters of the image labeling process. In an example, the previously stored parameters may be computed from a reduced set of training data.

Abstract: Distributional information for a set of ? vectors is determined using a sparse basis selection approach to representing an input image or video. In some examples, this distributional information is used for a classification task.

Abstract: Provided is a server system in which real-time communication based on a moving image group is performed between a number of users. The moving image collection/delivery server system accumulates moving images sequentially uploaded in real time via a network in a database, generates moving image thumbnails used to simultaneously display a plurality of moving images on a single display screen in real time, allocates the moving image thumbnails to a channel group adaptively generated from sequentially uploaded moving images, and delivers the moving image thumbnails to a terminal having a display and operating unit in real time via a network, and the sequentially generated moving image thumbnails are stored in an allocated channel in ascending order of shooting times and delivered to a plurality of terminals.

Abstract: Methods, machine-readable media, and devices for face image prioritization based on face quality analysis are described herein. For example, one or more embodiments include detecting a facial image in an image that has been acquired by a camera that monitors a scene, passing the facial image through a number of quality analysis filters that include a number of quality analysis factors, wherein processing complexity associated with the number of quality analysis factors increases consecutively, and submitting the facial image to a facial recognition program upon a determination that the facial image has passed the number of quality analysis filters.

Abstract: A method for authenticating the identity of a handset user is provided. The method includes: obtaining, a login account and a password from the user; judging whether the login account and the password are correct; if the login account or the password is incorrect, refusing the user to access an operating system of the handset; if the login account and the password are correct, sending the login account and the password to a cloud server, wherein the login account and the password correspond to a face sample image library of the user stored on the cloud server; acquiring an input face image of the user; sending the input face image to the cloud server; authenticating, by the cloud server, the identity of the user according to the login account, the password and the input face image.

Abstract: In a particular embodiment, a method includes applying a first feature detector to a portion of an image to detect a first set of features. The first set of features is used to locate a region of interest, and a boundary corresponding to the region of interest is determined. The method also includes displaying the boundary at a display. In response to receiving user input to accept the displayed boundary, a second feature detector is applied to an area of the image encapsulated by the boundary.

Abstract: Embodiments generally relate to summarizing a photo album in a social network system. In one embodiment, a method includes grouping photos into a plurality of groups of photos, and selecting a plurality of representative photos, where each representative photo represents a respective group from the plurality of groups, where the selecting is based on a quality score of each of the photos, and where each quality score is based on different types of attributes. The method also includes enabling the plurality of representative photos to be shared.

Abstract: A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Abstract: A mobile communication terminal for providing a group picture of a group in a phonebook, a method of generating the group picture in the phonebook of the mobile communication terminal and a method of performing a communication event using the group picture are provided. The method of generating a group picture in a phonebook of a mobile communication terminal includes selecting an original picture in which an image corresponding to each member of a group is displayed in a predetermined area corresponding to the group member, mapping an address of each group member to the corresponding predetermined area in the original picture and storing the original picture to which the group members' addresses are mapped as a group picture of the phonebook. The present apparatus and method enable easier editing and use of a group of a phonebook and characteristic decoration thereof which allow for more efficient use of the mobile terminal.

Abstract: In accordance with one embodiment, a method to track persons includes generating a first and second set of facial coefficient vectors by: (i) providing a first and second image containing a plurality of persons; (ii) locating faces of persons in each image; and (iii) generating a facial coefficient vector for each face by extracting from the images coefficients sufficient to locally identify each face, then tracking the persons within the images, the tracking including comparing the first set of facial coefficient vectors to the second set of facial coefficient vectors to determine for each person in the first image if there is a corresponding person in the second image. Optically the method includes using estimated locations in combination with the vector distance between facial coefficient vectors to track persons.

Abstract: This disclosure pertains to devices, methods, systems, and computer readable media for generating and/or interpreting image metadata to determine input parameters for various image processing routines, e.g., filters that distort or enhance an image, in a way that provides an intuitive experience for both the user and the software developer. Such techniques may attach the metadata to image frames and then send the image frames down an image processing pipeline to one or more image processing routines. Image metadata may include face location information, and the image processing routine may include an image filter that processes the image metadata in order to keep the central focus (or foci) of the image filter substantially coincident with one or more of the faces represented in the face location information. The generated and/or interpreted metadata may also be saved to a metadata track for later application to unfiltered image data.

Abstract: Methods, systems and apparatus for refining image annotations. In one aspect, a method includes receiving, for each image in a set of images, a corresponding set of labels determined to be indicative of subject matter of the image. For each label, one or more confidence values are determined. Each confidence value is a measure of confidence that the label accurately describes the subject matter of a threshold number of respective images to which it corresponds. Labels for which each of the one or more confidence values meets a respective confidence threshold are identified as high confidence labels. For each image in the set of images, labels in its corresponding set of labels that are high confidence labels are identified. Images having a corresponding set of labels that include at least a respective threshold number of high confidence labels are identified as high confidence images.

Abstract: A method includes obtaining an image to be compared at the time of use which is intended for comparing biometric information at the time of use of a user, obtaining posture information at the time of use which indicates a posture when biometric information at the time of use of the user is shot, performing personal authentication for the user by comparing the image to be compared at the time of use with a preregistered image to be compared at the time of registration of the user, and inputting an operation instruction corresponding to operation information preregistered in association with posture information at the time of registration which is determined to match, by comparing the posture information at the time of use with a plurality of pieces of preregistered posture information at the time of registration of the user.

Abstract: In an object detection apparatus 1, a window definer 11 defines a window relative to the location of a pixel in an input image 20. A classification value calculator 13 calculates a classification value indicative of the likelihood that a detection target is present in the window image contained in the window based on the feature data of the detection target. A classification image generator 14 arranges the classification value calculated from the window image according to the pixel location to generate a classification image. An integrator 15 integrates the classification image and a past classification image 42 generated from a past input image input prior to the input image 20 to generate an integrated image 45. A determiner 16 determines whether the detection target is present in the input image 20 based on the integrated image 45.

Abstract: A method and device for converting 3D images are disclosed herein. The method include the steps of analyzing a 2-dimensional (2D) image and generating 2D image information, generating a global depth and a local depth by using the generated 2D image information, the global depth corresponding to 3D effect pattern information of the 2D image and the local depth corresponding to 3D effect information of an object included in the 2D image, and generating a depth map by using the global depth and the local depth, wherein the depth map corresponds to 3D effect information of the 2D image, and generating a 3D image configured of a left-eye image and a right-eye image by using the 2D image and the depth map.

Abstract: A system for automatically selecting a template and a number of secondary images for display with a primary preselected image based on analyzing the primary image's attribute information and comparing the template's required image attributes and secondary image's attribute information. The attribute information is used to evaluate and arithmetically score a compatibility of the images and template so that a best compatibility fit can be obtained when displaying the image.

Abstract: A method, system, and/or computer program product classifies a plurality of objects displayed on a display device and displays the classified plurality of objects. The method comprises: selecting one object from a plurality of objects as a selected object; and classifying the plurality of objects, where classification of each of the plurality of objects is based on a degree of association with the selected object. Classifying the plurality of objects comprises: reading a feature setting for computation of the degree of association; specifying a plurality of features that are subject to the computation of the degree of association; computing, for each of the plurality of objects, the degree of association based on a difference in feature value from the selected object with regard to the plurality of features; and dividing the plurality of objects into a plurality of groups, where each group is based on the degree of association.

Abstract: A method on a computing device for categorizing one or more blocks of an image is disclosed. The method includes computing a membership value of each of the one or more blocks for each of one or more categories based on a set of parameters associated with each of the one or more blocks. The one or more categories comprise at least an image category. Each of the one or more blocks is categorized in the one or more categories based on the membership value. A category of at least one block is modified to the image category based on a reference signal and the membership value such that the number of blocks categorized under the image category increases.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: Aspects of the present invention include object detection training systems and methods and using object detection systems and methods that have been trained. Embodiments presented herein include hybrid learning approaches that combine global classification and local adaptations, which automatically adjust model complexity according to data distribution. Embodiments of the present invention automatically determine model complexity of the local learning algorithm according to the distribution of ambiguous samples. And, embodiments of the local adaptation from global classifier avoid the common under-training problem for local classifier.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: A method, apparatus and computer program product are provided for identifying an unknown subject using face recognition. In particular, upon receiving a plurality of images depicting a subject, the method may include deriving and storing a common component image and a gross innovation component image associated with the subject, wherein the subject can later be identified in a new image using these two stored images. The common component image may capture features that are common to all of the received images depicting the subject, whereas the gross innovation component image may capture a combination of the features that are unique to each of the received images. The method may further include deriving and storing a low-rank data matrix associated with the received images, wherein the low-rank data matrix may capture any illumination differences and/or occlusions associated with the received images.

Abstract: An information processing apparatus comprising: an obtaining unit configured to obtain image data; a detection unit configured to detect an object from the image data; an attribute determination unit configured to determine an attribute indicating a characteristic of the object detected by the detection unit; a registration unit configured to register the image data in at least one of a plurality of dictionaries based on the attribute determined by the attribute determination unit; and an adding unit configured to add, when the image data is registered in not less than two dictionaries, link information concerning the image data registered in the other dictionary to the image data registered in one dictionary.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: An object detection apparatus mounted in a system for detecting a target object in various changing environmental conditions. In the apparatus, an acquirer acquires either or both of information indicative of an external environment around the system and information indicative of an operating state of the system. A determiner determines an environmental condition with respect to an input image according to the acquired information. A setter sets, for each of plural image recognition methods each being a combination of one of the plural image recognition dictionaries and one of the plural image recognition techniques, a weighting factor according to the environmental condition determined by the determiner. A detector detects the object in the input image by applying each of the plural image recognition methods to the input image to obtain recognition results reflecting the weighting factors, and collectively evaluating the recognition results.

Abstract: A method of classifying the shot type of a video frame, comprising loading a frame, dividing the frame into field pixels and non-field pixels based on a first playfield detection criteria, determining an initial shot type classification using the number of the field pixels and the number of the non-field pixels, partitioning the frame into one or more regions based on the initial classification, determining the status of each of the one or more regions based upon the number of the field pixels and the non-field pixels located within each the region, and determining a shot type classification for the frame based upon the status of each the region.

Abstract: 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, the image from the plurality 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 two images in a numeric representation, the numeric representation embodying similarities found between each of the plurality of images. 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.

Abstract: Methods, systems, and apparatus including computer program products for using extracted image text are provided. In one implementation, a computer-implemented method is provided. The method includes receiving an input of one or more image search terms and identifying keywords from the received one or more image search terms. The method also includes searching a collection of keywords including keywords extracted from image text, retrieving an image associated with extracted image text corresponding to one or more of the image search terms, and presenting the image.

Abstract: Methods and systems for measuring group behavior are provided. Group behavior of different groups may be measured objectively and automatically in different environments including a dark environment. A uniform visible signal comprising images of members of a group may be obtained. Facial motion and body motions of each member may be detected and analyzed from the signal. Group behavior may be measured by aggregating facial motions and body motions of all members of the group. A facial motion such as a smile may be detected by using the Fourier Lucas-Kanade (FLK) algorithm to register and track faces of each member of a group. A flow-profile for each member of the group is generated. Group behavior may be further analyzed to determine a correlation of the group behavior and the content of the stimulus. A prediction of the general public's response to the stimulus based on the analysis of the group behavior is also provided.

Abstract: In accordance with an embodiment, a method of comparing a first group of frames to a second group of frames includes electronically receiving the first group of frames, selecting a group of frames from the first group of frames as a first key frame set, calculating a hash distance between an image hash for each frame in the first key frame set to an image hash of each frame of a second key frame set taken from second group of frames, and choosing frames in the first group of frames with a minimum hash distances to respective reference frames to form a series of minimum hash distances.

Abstract: Described is a linear structure from motion technique that is scalable, parallelizable, treats images equally, and is robust to outliers, without requiring intermediate bundle adjustment. Camera rotations for images are estimated using feature point correspondence and vanishing points matched across the images. The camera rotation data is fed into a linear system for structure and translation estimation that removes outliers and provides output data corresponding to structure from motion parameters. The data may be used in further optimization e.g. with a final non-linear optimization stage referred to as bundle adjustment to provide final refined structure from motion parameters.

Abstract: A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.

Abstract: Described is a system for multiple-object recognition in visual images. The system is configured to receive an input test image comprising at least one object. Keypoints representing the object are extracted using a local feature algorithm. The keypoints from the input test image are matched with keypoints from at least one training image stored in a training database, resulting in a set of matching keypoints. A clustering algorithm is applied to the set of matching keypoints to detect inliers among the set of matching keypoints. The inliers and neighboring keypoints in a vicinity of the inliers are removed from the input test image. An object label and an object boundary for the object are generated, and the object in the input test image is identified and segmented. Also described is a method and computer program product for multiple-object recognition in visual images.

Abstract: A system for determining piles comprises an interface and a processor. The interface is configured to receive an image. The processor is configured to determine one or more attributes of the image; to determine whether the image is a member of a top of a hierarchy based at least in part on the attributes. In the event it is determined that the image is a member of the top of the hierarchy,: determine a set of elements of the hierarchy the image is a member of, based at least in part on the attributes and determine which of the set of entities are piles.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing age estimation. In one aspect, a method includes receiving an image of a person, submitting the image to multiple binary classifiers that are each trained to classify the person in the image as belonging to one of two predefined age groups, or as belonging or not belonging to a particular age group, where each output includes a confidence value associated with classifying the person in the image, obtaining the confidence values from the multiple binary classifiers, aggregating the confidence values, and generating an age estimation for the person in the image based on the aggregated confidence values.

Abstract: Candidate identification utilizing fingerprint identification is disclosed.

Abstract: The identification of known normal structures within an image is preferably accomplished using an appearance model. Specifically, an active appearance model, which encapsulates a complete model of the shape and global texture variations of an object from a collection of samples, is utilized to define normal structures within an image by restricting training samples supplied to the active appearance model during a training phase to those that do not contain abnormal structures. Accordingly, the trained appearance model represents only normal variations in the object of interest. When another image with abnormalities is presented to the system, the appearance model cannot synthesize the abnormal structures which show up as errors in a residual image. Accordingly, the errors in the residual image represent potential abnormalities.

Abstract: A method of making an image-based product includes storing a plurality of digital images; providing one or more image distributions, each image distribution corresponding to a theme and including a distribution of image types related to the theme; selecting a theme having a corresponding image distribution, the image distribution having a distribution of image types; using a computer to select digital images from the plurality of digital images, the selected digital images having the image distribution corresponding to the selected theme; and assembling the selected digital images into an image-based product.

Abstract: A case database management system capable of maintaining high quality case information registered in a case DB. A user of the case database management system can not only refer to case information registered in the case DB by user terminals, but also evaluate the case information. The case DB server of the system collects evaluation information evaluated by the user for each case information item and assigns each case information item with an evaluation containing an evaluation point indicating usefulness of the case information. This enables the case database management system to register more highly evaluated (high quality) case information in the case DB and to provide the user with useful case information.

Abstract: To reduce a display time of an image for which an observation is less required, and to effectively perform an observation of a series of images, the image display apparatus (1) includes an image processing controller (2a) that acquires an image from a storage unit (5), controls various image processes for the acquired image, and stores an image of a processing result in the storage unit (5), an image classification unit (2b) that calculates a correlation value between temporally continuous images and classifies each of the images into an image group based on the calculated correlation value, an image-of-interest detecting unit (2c) that detects a feature-image area including a predetermined feature from each of the images, and detects the feature image including the detected feature-image area as an image-of-interest, a representative-image extracting unit (2d) that extracts the image-of-interest and a first image in each of the image groups as a representative image and sets a display rate for each of the ex

Abstract: A completely automated method for classifying electronic images of commercial documents such as invoices, bills of lading, explanations of benefits, etc. is based on the layout of the documents. A document is classified as belonging to a class of similarly laid out images if its distance, determined by a set of pre-defined metrics, to any of the template layouts from the same class of template layouts does not exceed a certain user-defined threshold. Alternatively, if the sum of distances from a given image to several template layouts from the same class does not exceed a user-selected threshold, the image is deemed to belong to a class of images with a specific layout.

Abstract: Systems, methods and apparatus for image processing by classifying pixels are described. In some systems, an exposure control operation is performed according to the pixel classifications. In some cases, the pixels are classified according to a predetermined segmentation of a color space, based on predicted sensor responses.

Abstract: Systems and methods for generating a derived biometric template are provided. A biometric sample of a user is determined. A base biometric template includes a plurality of features extracted from the biometric sample. A degree of rotation is determined, and a derived biometric template is generated using the degree of rotation.

Abstract: A system for detecting a vehicle occupancy violation includes an image capture module that acquires an image including a vehicle cabin from a camera positioned to view oncoming traffic. The system includes a violation determination device, which includes a feature extraction module that processes the image pixels for determining an image descriptor. The process is selected from a group consisting of a Successive Mean Quantization Transform; a Scale-Invariant Feature Transform; a Histogram of Gradients; a Bag-of-Visual-Words Representation; a Fisher Vector Representation; and, a combination of the above. The system further includes a classifier that determines a distance that the vehicle image descriptor/representation is positioned in the projected feature space relative to a hyper-plane. The classifier determines whether the distance meets a threshold and classifies the image when the threshold is met. A processor implements the modules. A graphic user interface outputs the classification.

Abstract: Moving objects are classified based on maximum margin classification and discriminative probabilistic sequential modeling of range data acquired by a scanner with a set of one or more 1D laser line scanner. The range data in the form of 2D images is pre-processed and then classified. The classifier is composed of appearance classifiers, sequence classifiers with different inference techniques, and state machine enforcement of a structure of the objects.

Abstract: A sort-out cycle such as a judgment of three levels of “usable”, “unusable”, and “reserve” is made to images and, when the judgment of all images is completed, the judgment of the three levels is made again to the “reserve” images is repeated. The number of sort-out cycle times in which the judgment is made is applied as an evaluation to the images which are finally determined as “usable”.

Abstract: An image processing apparatus includes: a determining unit configured to determine one or more pieces of image data to be processed; a motion detecting unit configured to detect the motion of the image processing apparatus; a processing unit configured to execute predetermined processing regarding the one or more pieces of image data determined via the determining unit; and a processing control unit configured to execute control so as to execute the predetermined processing via the processing unit, in the case of detecting that a particular operation has been executed as to the image processing apparatus via the motion detecting unit.