Abstract: The subject disclosure is directed towards performing connected components in hardware, such as an FPGA, which is facilitated by a linked list structure that does not grow. During a connected components graph labeling process, when a merge is encountered, the data structure comprising labels and associated equivalency data swaps the equivalency data of the two vertices whose different labels produced the merge condition.

Abstract: A social network application may identify images having common links between a first user's image collection and a second user's image collection. The common links may be identified through metadata or similar portions of the images. Using the first user's image collection, elements of interest may be identified and compared to a second user's image collection to find matches. When matches are found, the results may be selected from groups of results to show a diverse set of matches. The user may be presented with options to select and add matched images to the user's collection, as well as to browse more images that match one or more of the groups.

Abstract: An image processing device includes an information acquisition section that acquires a photographing position of a photographed image or a position of an imaging device as coordinate information, a distribution state acquisition section that acquires a distribution state of a plurality of pieces of coordinate information acquired as the coordinate information, and a keyword assignment section that assigns a keyword that corresponds to the acquired distribution state to the photographed image.

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: In embodiments of photo importance determination, a photo analyzer is implemented to analyze the image content of each photo in a set of digital photos, and determine similar photos based on the image content and metadata of the digital photos. The photo analyzer can then create stacks of the similar photos and determine a representative photo from the similar photos in each stack. The photo analyzer can then determine a display sequence to display non-stacked photos and the representative photos of each stack. The photo analyzer can also receive viewer feedback associated with the digital photos being displayed for viewing, and then determine a different representative photo from the similar photos in each of the stacks based on the viewer feedback. The photo analyzer can also determine a revised display sequence of the non-stacked photos and the representative photos of the stacks based on the viewer feedback.

Abstract: Various systems, methods, and programs embodied in computer-readable mediums are provided for detecting a match in patterns. In one embodiment, a method is provided that comprises performing a fractal analysis on a first pattern in a computer system to generate a first global quantitative characterization of the first pattern. The method further comprises comparing the first global quantitative characterization with a second global quantitative characterization associated with a second pattern in the computer system to determine whether the first pattern matches the second pattern. The second global quantitative characterization is generated from the second pattern.

Abstract: A computer-implemented technique can receive, at a computing device including one or more processors, a plurality of photos. The technique can extract quality features and similarity features for each of the plurality of photos and can obtain weights for the various quality features and similarity features based on an analysis of a reference photo collection. The technique can generate a quality metric for each of the plurality of photos and can generate a similarity matrix for the plurality of photos by analyzing the various quality features and similarity features and using the obtained weights. The technique can perform joint global maximization of photo quality and photo diversity using the quality metrics and the similarity matrix in order to select a subset of the plurality of photos having a high degree of representativeness. The technique can then store the subset of the plurality of photos in a memory.

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: A computer-assisted method of classifying cytological samples, includes using a processor to analyze images of cytological samples and identify cytological objects of interest within the sample images, wherein the processor (i) displays images of identified cytological objects of interest from the sample images to a reviewer, (ii) accesses a database of images of previously classified cytological objects, and (iii) displays to the reviewer, interspersed with the displayed images of the identified objects of interest from the sample images, one or more images obtained from the database of images of previously-classified objects.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Candidate identification utilizing fingerprint identification is disclosed.

Abstract: Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

Abstract: An information processing apparatus that executes information display processing is disclosed. The apparatus includes: a display unit that executes information display; and a data processing unit that executes a control of the information display of the display unit and data processing based on a user input. The data processing unit displays an image browsing screen, which corresponds to image data stored in a storage unit, on the display unit and executes display of a map at the position, which corresponds to position information included in attribute information of a selected image, on the basis of a user's input of image selection information and map display request.

Abstract: A digital image of the object is captured and the object is recognized from plurality of objects in a database. An information address corresponding to the object is then used to access information and initiate communication pertinent to the object.

Abstract: A computer implemented system for identifying license plates and faces in street-level images is disclosed. The system includes an object detector configured to determine a set of candidate objects in the image, a feature vector module configured to generate a set of feature vectors using the object detector to generate a feature vector for each candidate object in the set of candidate objects, a composite feature vector module to generate a set of composite feature vectors by combining each generated feature vector with a corresponding road or street description of the object in question, and an identifier module configured to identify objects of a particular type using a classifier that takes a set of composite feature vectors as input and returns a list of candidate objects that are classified as being of the particular type as output.

Abstract: A plurality of images related to a particular image are determined, and a plurality of annotations associated with the plurality of images are identified. An ontology is determined based on the plurality of annotations, and an image annotation for the particular image is determined based on the ontology. The ontology may be a semantic ontology, for example.

Abstract: A method is described for determining eye location on a frontal face digital image in order to validate the frontal face as such and determine points of reference. The method consists of the following steps: obtain a frontal face digital image that is W pixels wide and H pixels high in grayscale, where the top left vertex of the digital image is considered to be the reference source; detect each eye, starting from a search subimage of the frontal face digital image, delivering approximate coordinates if both eyes have been detected, or an arbitrary predetermined value to indicate no eye detection; locate the iris of each eye, if both eyes have been detected, starting from the approximate coordinates, validating the frontal face digital image by delivering eye positions; or, validate the frontal face digital image if one or both eyes have not been detected by submitting a search subimage defined by an area resulting from centering a square of area L in the original image, where L=0.

Abstract: A system, apparatus, and method for video categorization using heterogeneous signals are disclosed. One aspect of the disclosed embodiments is a method for categorizing a plurality of video streams. The method includes determining a plurality of features of at least some of the plurality of video streams, determining a category of a first video stream of the plurality of video stream based on its plurality of features, identifying, using a processor, a relationship between the first video stream and a second video stream of the plurality of videos, the relationship having an associated weight, and updating, using the processor, the category of the first video stream based on a first message determined using the associated weight.

Abstract: Various systems, methods, and programs embodied in computer-readable mediums are provided for the detection of patterns. In one embodiment, a pattern detection method is provided that comprises the step of performing a fractal analysis of a pattern to generate a plurality of scaling parameters from a fractal associated with the pattern in a computer system. In addition, the method further comprises the step of detecting a degree of organization in the pattern by examining a degree of equality among the scaling parameters of the fractal in the computer system.

Abstract: A method, system, and computer program product for detecting objects. Members of a general class of objects are searched for in a number of images. Members of a specific class of objects are searched for in a number of regions in the number of images where the number of regions contains at least a portion of the members of the general class. A member in the members of the specific class is a potential threat to a rotorcraft.

Abstract: Described is a system for object detection using classification-based learning. A fusion method is selected, then a video sequence is processed to generate detections for each frame, wherein a detection is a representation of an object candidate. The detections are fused to generate a set of fused detections for each frame. The classification module generates a classification score labeling each fused detection based on a predetermined classification threshold. Otherwise, a token indicating that the classification module has abstained from generating a classification score is generated. The scoring module produces a confidence score for each fused detection based on a set of learned parameters from the learning module and the set of fused detections. The set of fused detections are filtered by the accept-reject module based on one of the classification score or the confidence score. Finally, a set of final detections representing an object is output.

Abstract: Systems and methods are provided for detecting anatomical components in images. In accordance with one implementation, at least one anchor landmark is detected in an image. The position of the anchor landmark is used to detect at least one bundle landmark in the image. In accordance with another implementation, at least two neighboring landmarks are detected in an image, and used to detect at least one anatomical primitive in the image.

Abstract: Methods and systems for automating insertion of content into media-based projects are disclosed. In one embodiment, images are uploaded from a user device to a server. The server performs image analysis on the uploaded images and obtains automatic image information. After presentation of the initial project, the user manually interacts with the uploaded images to provide user inputs to the project. The system observes the user interactions to generate manual image information based on the user interactions with the project. Based on the automatic image information and the manual image information, the present invention automatically updates a printable photobook or other media-based project after automatically inserting relevant content into the project. This produces a faster and more seamless user experience, and increases the number of customers purchasing the ultimate product by minimizing friction points to purchase.

Abstract: A moving object detection device includes a window setting unit configured to set a window having a predetermined volume in a video, an orientation of spatial intensity gradient calculation unit configured to calculate, for each pixel included in the window, an orientation of spatial intensity gradient, a spatial histogram calculation unit configured to calculate a spatial histogram that is a histogram of the orientation of spatial intensity gradient within the window, an orientation of temporal intensity gradient calculation unit configured to calculate, for each pixel included in the window, an orientation of temporal intensity gradient, a temporal histogram calculation unit configured to calculate a temporal histogram that is a histogram of an orientation of temporal intensity gradient within the window, and a determination unit configured to determine whether or not the moving object is included within the window based on the spatial histogram and the temporal histogram.

Abstract: A system has an aerial image database containing sensor data representing a plurality of aerial images of an area having multiple sub-areas. A processor applies a classifier to the sensor values to identify a label for each sub-area in each aerial image and to thereby generate an initial label sequence for each sub-area. The processor identifies a most likely land cover state for each sub-area based on the initial label sequence, a confusion matrix and a transition matrix. For each sub-area, the processor stores the most likely land cover state sequence for the sub-area.

Abstract: Aspects of the present invention include feature point matching systems and methods. In embodiments, a tree model is used to find candidate matching features for query feature points. In embodiments, the tree model may be pre-learned using a set of sample images, or alternatively, the tree model may be constructed using one or more of the input images. In embodiments, features in one of the stereo images are registered with the tree model, and then features from the other stereo image are queried through the tree model to identify their correspondences in the registered stereo image. As compared to prior brute force matching methodologies, embodiments disclosed herein reduce the complexity and calculation time for determining matching feature points in stereo images.

Abstract: A method for recognition of a predetermined pattern in an image data set recorded by a device for recording of at least two electromagnetic frequency spectra is provided. A first difference value is formed for the image points of the selected area as a function of a difference between a data vector of a corresponding image point and a first reference data vector. A second difference value is formed for an image point of a selected area as a function of a difference between the data vector of this image point and a second reference data vector. A predetermined pattern is recognized when it is determined at least one pattern correlation quantity is below a predetermined threshold value and a local minimum is present.

Abstract: Disclosed are various embodiments for performing an analysis on images of a product packaging. Capture of at least one image of at least one side of a packaging in initiated. An image analysis is performed on the at least one image. Product attributes are detected based at least upon the at least one image analysis and associated with the product in a data store.

Abstract: An auto-commissioning system provides automatic parameter selection for an intelligent video system based on target video provided by the intelligent video system. The auto-commissioning system extracts visual feature descriptors from the target video and provides the one or more visual feature descriptors associated with the received target video to an parameter database that is comprised of a plurality of entries, each entry including a set of one or more stored visual feature descriptors and associated parameters tailored for the set of stored visual feature descriptors. A search of the parameter database locates one or more best matches between the extracted visual feature descriptors and the stored visual feature descriptors. The parameters associated with the best matches are returned as part of the search and used to commission the intelligent video system.

Abstract: An improved branch-and-bound process of interval arithmetic subdivision in furtherance of computation of rigorous error bounds on integrated digital scene information for two dimensional display is provided. More particularly, a first aspect of the subject process includes pseudo-randomly subdividing an interval domain comprising a set of interval variables in furtherance of ascertaining a characteristic contribution of the interval variables of said set of interval variables to an image space comprising at least a sub-pixel area. A further aspect, either alone or in combination with the first aspect contemplates pseudo-randomly discarding a select partitioning of interval variables of a set of interval variables of a geometric function from a computed solution of an interval arithmetic branch-and-bound process.

Abstract: The illustrative embodiments described herein provide systems and methods for movie identification based on a location. In the embodiment, a method includes locating a mobile communication device associated with a user to form location data, accessing a location database to determine a geographic location of the mobile communication device based on the location data, and identifying a set of movies related to the geographic location by accessing a movie database. Each of the set of movies in the movie database is associated with one or more respective geographic locations. The method also includes presenting a set of movie results corresponding to the set of movies on a graphical user interface of the mobile communication device. In another embodiment, the method may also validate an object photographed by a camera of the mobile communication device, and use the recognized object to identify the set of movies.

Abstract: Provided are methods for determining and analyzing photometric and morphometric features of small objects, such as cells to, for example, identify different cell states. In particularly, methods are provided for identifying apoptotic cells, and for distinguishing between cells undergoing apoptosis versus necrosis.

Abstract: The image classification apparatus extracts first features of each received image (S22) and second features of a relevant image relevant to each received image (S25). Subsequently, the image classification apparatus obtains a third feature by calculation using locality of the extracted first and second features, the third feature being distinctive of a target object of each received image (S26), and creates model data based on the obtained third feature (S27).

Abstract: Described is a system for object and behavior recognition which utilizes a collection of modules which, when integrated, can automatically recognize, learn, and adapt to simple and complex visual behaviors. An object recognition module utilizes a cooperative swarm algorithm to classify an object in a domain. A graph-based object representation module is configured to use a graphical model to represent a spatial organization of the object within the domain. Additionally, a reasoning and recognition engine module consists of two sub-modules: a knowledge sub-module and a behavior recognition sub-module. The knowledge sub-module utilizes a Bayesian network, while the behavior recognition sub-module consists of layers of adaptive resonance theory clustering networks and a layer of a sustained temporal order recurrent temporal order network. The described invention has applications in video forensics, data mining, and intelligent video archiving.

Abstract: A computer-implemented method for processing one or more video frames may include obtaining one or more video frames; generating one or more blobs using the one or more video frames; classifying the one or more blobs to produce one or more classified blobs, wherein the one or more classified blobs include one or more of a stationary target, a moving target, a target insertion, a target removal, or a local change; and constructing a list of detected targets based on the one or more classified blobs.

Abstract: Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.

Abstract: The present invention uses dynamic grouping to divide up training samples to train different classification nodes. At the beginning of the training, all samples are in the same group. A clustering process is applied in the feature space of the selected feature vectors with cluster indexes accumulated. The average of all the accumulated cluster indexes is used as the threshold for splitting the samples into two groups. When the splitting criterion is met, samples are split into two groups based on their similarity in the feature space.

Abstract: In an example embodiment, a method is provided for image categorization. Here, images are displayed. In turn, a user input that describes a characteristic shared between the images from a comparison between the images is received. The user input may then be classified into categorization data.

Abstract: In real biometric systems, false match rates and false non-match rates of 0% do not exist. There is always some probability that a purported match is false, and that a genuine match is not identified. The performance of biometric systems is often expressed in part in terms of their false match rate and false non-match rate, with the equal error rate being when the two are equal. There is a tradeoff between the FMR and FNMR in biometric systems which can be adjusted by changing a matching threshold. This matching threshold can be automatically, dynamically and/or user adjusted so that a biometric system of interest can achieve a desired FMR and FNMR.

Abstract: A processor is described that includes an instruction execution pipeline having an instruction fetch unit to fetch and decode an instruction. The processor also has an execution unit to execute the instruction. The execution unit has a state machine and content addressable memory (CAM) circuitry. The state machine is to receive a pointer to a stream of DEFLATE encoded information, fetch a section of the DEFLATE encoded information and apply the section of the DEFLATE encoded information to the CAM to obtain decoded DEFLATE information.

Abstract: The present disclosure relates to a method and apparatus for classifying pixels in an input image and image processing system. The apparatus includes a first confidence determination unit configured for determining a first confidence of each of the pixels in the input image belonging to each of a plurality of predetermined classes; a detection unit configured for acquiring, by using a detector for detecting a particular class among the plurality of predetermined classes, detection results used to indicate pixels which belong to the particular class; an updating unit configured for updating the first confidences according to the detection results; and a class determination unit configured for determining the class to which each of the pixels in the input image belongs among the plurality of predetermined classes, according to the updated first confidences.

Abstract: An image including a face is input (S201), a plurality of local features are detected from the input image, a region of a face in the image is specified using the plurality of detected local features (S202), and an expression of the face is determined on the basis of differences between the detection results of the local features in the region of the face and detection results which are calculated in advance as references for respective local features in the region of the face (S204).

Abstract: Techniques for a shape descriptor used for object recognition are described. Tokens of an object in digital image data are captured, where tokens can be edges, interest points or even parts. Geometric configurations of the tokens are captured by describing portions of the shape of the object. The shape of such configurations is finely quantized and each configuration from the image is assigned to a quantization bin. Objects are recognized by utilizing a number of quantization bins as features. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: A computer-implemented segmentation method is used to process an image representing a plurality of nuclei. The method is implemented in a computer having a processor and a physical memory. A set of instructions are provided to the processor the physical memory of the computer. The processor is configured by executing the set of instructions in the physical memory so as to automatically segment the image by: thresholding a grey-scale image to create a black and white image; identifying objects in the black and white image and removing objects failing to meet predetermined criteria; extracting objects; and applying an edge detector on the segmented image to identify the edges of the nuclei. Overlapping nuclei are split to improve results.

Abstract: An image similar to a target image is selected from among a set of candidate images. A set of image classifiers is first generated and used to create a fingerprint for each candidate image. A hash table is generated for each fingerprint segment, and an identifier for each candidate image is stored in each hash table based on the candidate image fingerprint value for the fingerprint segment associated with the hash table. A fingerprint is created for the target image using the set of classifiers. Segments of the target image fingerprints are compared to segments of the candidate image fingerprints using the hash table, and a candidate image similar to the target image is selected based on this comparison.

Abstract: 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.

Abstract: Described herein are an apparatus, system, and method for real-time identification of finger impressions for multiple users on a platform. The apparatus comprises a first logic unit to register finger impressions of corresponding multiple users, and to store the registered finger impressions of each user among the multiple users in a storage unit, wherein the finger impressions of the corresponding multiple users are received by a platform; and a second logic unit to identify in real-time active participants according to the registered finger impressions of each user among the multiple users, wherein the active participants are participants of an executing application.

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, 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.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The image capturing device has a field of view and produces image data representing an image of the field of view. The object classification module is operable to determine whether an object in an image is a member of an object class. The object classification module includes N decision steps configured in a cascade configuration, wherein at least one of the N decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether an object is a member of the object class.

Abstract: A system and method for classification of images of an image stream may include receiving an image stream of unclassified images, for example produced by an in-vivo imaging device, and based on indirect user input, adapting an initial classification algorithm to classify images to groups based on at least a subset of the received image stream of unclassified images. The indirect user input may be used to generate user-based indications for the classification.