Abstract: An image processing method is adapted to process images captured by at least two cameras in an image system. In an embodiment, the image processing method comprises: matching two corresponding feature points for two images, respectively, to become a feature point set; selecting at least five most suitable feature point sets, by using an iterative algorithm; calculating a most suitable radial distortion homography between the two images, according to the at least five most suitable feature point sets; and fusing the images captured by the at least two cameras at each of timing sequences, by using the most suitable radial distortion homography.

Abstract: A system and method for performing foreground/background separation on an input image. The method identifies a corresponding model visual element in a scene model associated with the input image, the model visual element being associated with a set of element models, each element model including a plurality of visual data sets. Select an element model from the set of element models, dependent upon a visual distance between the input visual element and a visual data set of the selected element model satisfying a predetermined criterion. The method classifies the input visual element as one of foreground and background, dependent upon the selected element model, and then updates each visual data set in the selected element model, dependent upon the input visual element and at least first and second different methods of updating a visual data set.

Abstract: Devices, systems and methods are disclosed that detect and interpret the meaning of visual indicators (such as LEDs) of devices, and provide information and solutions to potential malfunctions to users. For example, image data of the visual indicators can be captured by an image capture device (such as a camera). The image data may then be analyzed to identify an object captured in the image data. Luminescence of pixel values in the image data corresponding to a visual indicator of the object may be used to determine a sequence of the visual indicator. Then, information corresponding to the meaning of the sequence may be sent to a device of a user or owner of the object.

Abstract: Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn't particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression.

Abstract: A method for face recognition in the video comprises: performing feature extraction on a target face in multiple image frames in the video to generate multiple face feature vectors respectively corresponding to the target face in the multiple image frames; performing time sequence feature extraction on the plurality of face feature vectors to convert the plurality of face feature vectors into a feature vector of a predetermined dimension; and judging the feature vector of the predetermined dimension by using a classifier so as to recognize the target face.

Abstract: The invention provides a method and device for acquiring clothing image attribute points, which can increase an efficiency of acquiring the clothing image attribute points. The method comprises: saving five attribute points manually calibrated and determining first to eleventh points in sequence by taking a horizontal direction as a horizontal coordinate and a vertical direction as a vertical coordinate, for a first side of two bilaterally symmetric sides of the clothing image. By means of a technical solution of an embodiment of the present invention, for the clothing image, five points are manually calibrated first; and then the other eleven attribute points can be determined by a computer, so that a manual workload is reduced, and the efficiency of acquiring the clothing image attribute points is increased.

Abstract: Structure boundaries may be determined by receiving a plurality of three dimensional (3D) data points representing a geographic area. The 3D data points may be projected into a two dimensional (2D) grid comprised of area elements. A structure boundary may be determined based on an analysis of the area elements.

Abstract: A solution for inventory identification and quantification using a portable computing device (“PCD”) comprising a camera subsystem is described. An exemplary embodiment of the solution comprises a method that begins with capturing a video stream of a physical inventory comprised of a plurality of individual inventory items. Using a set of tracking points appearing in sequential frames, and optical flow calculations, coordinates for global centers of the frames may be calculated. From there, coordinates for identified inventory items may be determined relative to the global centers of the frames within which they are captured. Comparing the calculated coordinates for inventory items identified in each frame, as well as fingerprint data, embodiments of the method may identify and filter duplicate image captures of the same inventory item within some statistical certainty. Symbology data, such as QR codes, are decoded and quantified as part of the inventory count.

Abstract: The present disclosure pertains to the field of image processing technologies and discloses a face key point positioning method and a terminal. The method includes: obtaining a face image; recognizing a face frame in the face image; determining positions of n key points of a target face in the face frame according to the face frame and a first positioning algorithm; performing screening to select, from candidate faces, a similar face whose positions of corresponding key points match the positions of the n key points of the target face; and determining positions of m key points of the similar face selected through screening according to a second positioning algorithm, m being a positive integer. In this way, the problem that positions of key points obtained by a terminal have relatively great deviations in the related technologies is resolved, thereby achieving an effect of improving accuracy of positioned positions of the key points.

Abstract: The invention relates to method of detecting elliptical structures (10) in an image (9), comprising: detecting circular arc-shaped structures (11) in the image (9) using a circle Hough transform (CHT) of the image (9), wherein a radius and a center point (12) are determined for each circular arc-shaped structure (11), identifying pairs of circular arc-shaped structures (11) consisting of two of the detected circular arc-shaped structures (11) with substantially equal radii, defining, for each one of these pairs, a search area (14) within the image (9) depending on the center points (12) of the respective pair of circular arc-shaped structures (11), searching in the search area (14) defined for any given pair of circular arc-shaped structures (11), for a pair of edges (16) connecting these two circular arc-shaped structures (11). The invention further relates to a device for detecting elliptical structures in an image.

Abstract: A face recognition device includes a processor configured to: extract a plurality of feature points of a face included in an input image; detect a first and a second feature points that are paired from among the plurality of the feature points, a third feature point that is away from a straight line that connects the first and the second feature points, and two inter-feature vectors starting from the third feature point to the respective first the second feature points; calculate a feature angle formed by the two detected inter-feature vectors; and perform face recognition based on the feature angle formed by the two inter-feature vectors included in face information that is previously set as the face targeted for recognition and based on the calculated feature angle.

Abstract: The invention relates to an image processing method for automatically extracting distorted circular image elements (109; 601) from an image (101), comprising the steps of determining, by means of a circle detection (102), at least one circular image section (103; 301, 301?) as a first approximation of at least one distorted circular image element (109; 601), then determining, via a statistical color analysis of the at least one determined circular image section (301, 301?), whether the circular image section (301, 301?) belongs to one of one or more predefined color classes and, in case thereof, assigning the circular image section to the respective color class, and, finally, determining an exact shape (109; 601) of the at least one distorted circular element by starting with at least one of the one or more determined circular image sections assigned to one of the one or more color classes and varying the image section to maximize a value of a color-class specific function of the image section.

Abstract: Systems and methods for optimizing memory in an interest-driven business intelligence system in accordance with embodiments of the invention are illustrated. A dictionary for storing values of a dataset may be partitioned in accordance with some embodiments. The partitions of the dictionary may be generated by mapping and reducer processes. The mapping processes receive a value, determine the dimension of the data to which the value belongs, and provides the value to a reducer process that handles the determined dimension. Each reducer process generates partitions of the dictionary for each dimension. The number of values in each partition is determined and compared to a threshold value. Partitions that have a number of values greater than the threshold are stored in a common memory. Partitions smaller than the threshold value can be combined with other partitions such that the cardinality of the combined partition exceeds the threshold value.

Abstract: A reliability acquiring apparatus includes a section that stores information on a classifier that outputs part likelihood concerning a predetermined part of a detection target when applied to an image, a section that calculates, concerning an image region included in an input image, the part likelihood on the basis of the information on the classifier, a section that determines, on the basis of the calculated part likelihood, a position of the predetermined part in the input image, a section that stores information on a reference position of the predetermined part, a section that calculates, on the basis of the information on the reference position, difference information between the reference position of the predetermined part and the determined position of the predetermined part, and a section that calculates, on the basis of the difference information, reliability indicating possibility that the input image is an image of the detection target.

Abstract: The method includes for each current pair of first and second successive video images determining movement between the two images. The determining includes a phase of testing homography model hypotheses on the movement by a RANSAC type algorithm operating on a set of points in the first image and first assumed corresponding points in the second image so as to deliver one of the homography model hypothesis that defines the movement. The test phase includes a test of first homography model hypotheses of the movement obtained from a set of second points in the first image and second assumed corresponding points in the second image. At least one second homography model hypothesis is obtained from auxiliary information supplied by an inertial sensor and representative of a movement of the image sensor between the captures of the two successive images of the pair.

Abstract: Image processing apparatus includes a local amount generation unit, a correspondence point calculation unit, a relative correspondence point information calculation unit, a correspondence point selecting unit and a decision unit. The local feature value generation unit calculates, for a first image, a set of information about first local feature values including a first feature point(s). The correspondence point calculation unit calculates, for a second image, as information about correspondence point(s), a correspondence relation between the first and second feature point(s) contained in a set of information about second local feature values calculated from the second image.

Abstract: To reduce communication costs and computational complexity, only a subset of ranked SIFT points within a query image for a visual search request is transmitted to the visual search server in each iteration of an incremental search. For each candidate match, a flag identifying the matching points is returned by the server for use in computing holistic (e.g., histogram) information for a bounding box within the query image including the matching points. Distance from that query image holistic information is used to reject images from a short list used for a subsequent iteration, if any. If all images are rejected or a match criteria is met during one iteration, the search may terminate early without consideration of remaining SIFT points.

Abstract: Object detecting apparatus and method enable to easily and accurately generate a feature quantity for detecting an object at low cost. To achieve this, in the object detecting apparatus and method, a value is assigned in consideration of relations of rotation and reversal in uniform patterns by using a small-sized conversion table, and, an ULBP feature quantity after rotation and reversal is generated from a previous ULBP feature quantity by simple calculation without using a conversion table indicating a correspondence relation between previous values and values after rotation and reversal.

Abstract: A camera apparatus and method enabling a user to conveniently control an operation of the camera apparatus according to a gesture of a subject for photography input through a lens of a camera, and a method of controlling the camera apparatus are provided. The method includes generating a current frame by receiving an image input through a camera; comparing the current frame and one or more previous frames stored before the current frame and detecting a gesture of a subject for photography included in the current frame and the one or more previous frames; determining whether the gesture is a gesture UI; and when the gesture is the gesture UI, executing an operation corresponding to the gesture UI.

Abstract: A system for automatically characterizing areas of interest (e.g., urban areas, forests, and/or other compound structures) in high resolution overhead imagery through manipulation of a dictionary of visual words. The pixels of an input overhead image are initially clustered into a plurality of hierarchically-arranged connected components of a first hierarchical data structure. Image descriptors (e.g., shape, spectral, etc.) of the connected components are then clustered into a plurality of hierarchically-arranged nodes of a second hierarchical data structure. The nodes at a particular level in the second hierarchical data structure become a dictionary of visual words. Subsets of the visual words may be used to label the cells of a grid over the geographic area as falling into one of a number of areas of interest. Categorization information from the grid may be mapped into a resultant image whereby pixels depict their respective type of area of interest.

Abstract: A method includes receiving, at a processor from a camera, image data associated with a control panel. The method also includes presenting one or more images at a display. The one or more images are generated based on the image data. The method includes determining a location of a first control of the control panel based on the image data and based on control settings data. The method includes providing an indication of the location of the first control at the display, and providing an indication of a desired configuration of the first control at the display.

Abstract: A robot system includes a processing apparatus that detects one work from a plurality of works, and a robot that operates the detected one work. The processing apparatus includes a display unit that displays image data containing an image of the plurality of works captured by an imaging apparatus, a selection unit that selects a first image and a second image from the image data, and a processing unit that generates a model based on the first image and the second image and detects the one work using the generated model.

Abstract: To detect the presence of a logo in a search image, the positions of edges, in an image of the logo and in the search image, are represented by ratios of distances between the respective edge and a plurality of preceding edges. This can be carried out both horizontally and vertically. A sequence of edges at a selected position in the logo image is then compared with sequences of edges from a plurality of positions in the search image, and the position of the logo in the search image is determined from the position of a match between the respective sequences of edges.

Abstract: The techniques include an image recognition system to receive a realogram image including a plurality of organized objects and to detect and identify objects in the realogram image of one or more items on a retail shelf, identify shelf fronts and labels on the shelf fronts, identify empty space under shelves, identify areas where unidentified products may be, and identify areas where products are “out of stock”.

Abstract: Systems/apparatuses and methods are provided for creating aerial images. A three-dimensional point cloud image is generated from an optical distancing system. Additionally, at least one two-dimensional street level image is generated from at least one camera. The three-dimensional point cloud image is colorized with the at least one two-dimensional street level image, thereby forming a colorized three-dimensional point cloud image. The colorized three-dimensional point cloud image is projected onto a two-dimensional plane, using a processor, thereby forming a synthetic aerial image.

Abstract: A feature point matching device includes a position feature generation unit for generating a position feature quantity for a feature point based on a position of the feature point included in an image, a pixel value feature generation unit for generating a pixel value feature quantity for the feature point based on a pixel value of the feature point included in the image, and a feature matching unit for matching a first feature point and a second feature point based on a degree of matching between a first position feature quantity generated from a first image and a second position feature quantity generated from a second image and a degree of matching between a first pixel value feature quantity generated from the first image and a second pixel value feature quantity generated from the second image.

Abstract: The conference system (KS) can comprise at least one mobile terminal with a respective signaling apparatus which contains a sensor which detects motion by the signaling apparatus and outputs a corresponding motion signal. In addition, it can contain a motion signal processing apparatus which associates the motion signal from a gesture in nonverbal interhuman communication with a motion signal pattern and, as a result of an association having been made, produces an output signal with a corresponding piece of information about the type of detected gesture in the nonverbal interhuman communication. Furthermore, the conference system can include a transmission apparatus for supplying the output signal to a transmission channel which is respectively connected to a local area network. The network can include other network elements, such as a telephone for a moderator and a screen for conference control connected to it.

Abstract: A gesture recognition device that recognizes a gesture of a person from an image captured by a camera, and outputs information to an electronic apparatus in order to control the electronic apparatus based on the recognized gesture, has a person determination section that determines whether the person is included in the image, and an output section that outputs person out-of-view angle notification instruction information issuing an instruction to make a notification that the image of the person is not captured, when the person determination section determines that the person is not included in the image.

Abstract: An authentication device includes: a memory; and a processor coupled to the memory and configured to: acquire a plurality of images with regard to a certain living body, extract first biometric information from each of the plurality of images, perform an alignment between the plurality of images, based on the first biometric information, calculate a coefficient based on feature of the certain living body, the feature being identified by a result of the alignment, and generate a normalized image by normalizing at least one image from among the plurality of images based on the coefficient.

Abstract: To generate a high-quality mosaic image even in a case where a material image having a shape different from that of a region in which the material image is to be placed is used. A target image is divided into a plurality of different-sized regions. The size of a material image is changed in accordance with the area of corresponding one of the regions, and is placed in the region on the basis of the chromatic component of the region. In the case of overlapping material images that have been placed, control processing is performed so that a smaller one of them is placed on a larger one of them.

Abstract: An object of the exemplary embodiment is to make it possible to easily take a group photo of a plurality of persons including a photographer. In a digital camera 1, an image a of other photographed persons is taken by a photographer immediately after an instruction of a composite shooting mode is given. Next, the digital camera 1 is handed over to one of the other photographed persons, by whom an image b of the photographer is taken. When predetermined synthesizing conditions are satisfied, a facial image contained in one of the two taken images a and b shot in series is synthesized with the other taken image so that a new composite image c is created. Thus, a group photo in which all the persons have been photographed can be synthesized easily.

Abstract: An image processing device comprises a part-point specifying unit configured to specify a part point of an object; a feature-quantity extracting unit configured to extract one or a plurality of feature quantities from a pixel of a sampling point or from a pixel group including the pixel of the sampling point, for each of a plurality of sampling points, and extract candidate feature quantities corresponding to the part point constituted by the extracted plurality of feature quantities corresponding to the respective sampling points, the plurality of sampling points comprising the part point specified by the part-point specifying unit and at least one point on the image other than the part point; and a feature-quantity generating unit configured to generate one or a plurality of comparison feature quantities corresponding to the part points based on a predetermined standard by using the candidate feature quantities extracted by the feature-quantity extracting unit.

Abstract: To improve feature selection accuracy during a visual search, interest points within a query image are two-way matched to features in an affine transformed image or otherwise transformed version of the query image. A user device implements a method for selecting local descriptors in the visual search. The method includes: detecting a first set of interest points for the original image; computing an affine transform matrix; computing a new image as a transformation of the original image using the affine transform matrix; detecting a second set of interest points from the and new image; performing a two-way matching between the first set of interest points and the second set of interest points; sorting matching pairs according to a specified self-matching score (SMS); assigning an infinite value to SMS of unmatched interest points from the original image; selecting the interest points based on SMS. Significant performance gains reduce false positive matches.

Abstract: An information processing apparatus includes an information input section that reads input image information, a cumulative image information generator that generates cumulative image information for pixels corresponding to positions of pixels of a prescribed pixel pattern in the read input image information, and a memory controller that stores in the cumulative image information holding memory the cumulative image information generated in the cumulative image information generator. This configuration allows memory resources necessary for holding the cumulative image information to be reduced.

Abstract: A pattern extracting device, an image projecting device, a pattern extracting method, and a program capable of extracting all the feature points by interpolating defective feature points even when there are defective feature points of an image pattern. The pattern extracting device extracts feature points to be interpolated based on a captured image of a projected image pattern, and interpolates the feature point to be interpolated by using near-by feature points that are located near the feature point, divides the near-by feature points into groups, calculates extrapolation coordinates of the groups, and calculates coordinates of the feature point to be interpolated in view of significance of the extrapolation.

Abstract: Identification of objects in images. All images are scanned for key-points and a descriptor is computed for each region. A large number of descriptor examples are clustered into a Vocabulary of Visual Words. An inverted file structure is extended to support clustering of matches in the pose space. It has a hit list for every visual word, which stores all occurrences of the word in all reference images. Every hit stores an identifier of the reference image where the key-point was detected and its scale and orientation. Recognition starts by assigning key-points from the query image to the closest visual words. Then, every pairing of the key-point and one of the hits from the list casts a vote into a pose accumulator corresponding to the reference image where the hit was found. Every pair key-point/hit predicts specific orientation and scale of the model represented by the reference image.

Abstract: Certain aspects of the present disclosure relate to methods and apparatus for neuro-simulation with a single two-dimensional device to track objects. The neuro-simulation may report a point of interest in an image that is provided by the device. The device may center on the point of interest using one or more actuators. The simulation mechanism may input pixels and output a plurality of angles to the actuators to adjust their direction.

Abstract: A method of detecting feature points of an object in a system for motion detection includes obtaining a first image of the object from a first camera and a second image of the object from a second camera, extracting a foreground image from each of the first image and the second image, based on an assumption that the foreground image is a T-pose image, segmenting the foreground image into a first set of sections, identifying a first set of feature points associated with the first set of sections, obtaining a T-pose image with a set of predetermined feature points, and determining whether the foreground image is a T-pose image by comparing the first set of feature points with the set of predetermined feature points.

Abstract: A pattern processing apparatus includes an acquisition unit, an adjacent element identification unit, a connection unit, and an assigning unit. The acquisition unit acquires a pattern image in which a plurality of pattern elements are arrayed in a grid, together with coordinate information of the pattern elements. The adjacent element identification unit identifies, for each of the pattern elements thus acquired, adjacent elements, in at least two directions, each having the shortest distance to the each of the pattern elements. The distances between the each of the pattern elements and other pattern elements are calculated using the coordinate information. The connection unit connects and groups, as a connected component, the pattern elements that are adjacent to one another. The assigning unit assigns position information in the array in order from the pattern element which is a starting point, among the pattern elements included in the connected component, according to a predetermined regularity.

Abstract: A system for performing an image categorization procedure includes an image manager with a keypoint generator, a support region filter, an orientation filter, and a matching module. The keypoint generator computes initial descriptors for keypoints in a test image. The support region filter and the orientation filter perform respective filtering procedures upon the initial descriptors to produce filtered descriptors. The matching module compares the filtered descriptors to one or more database image sets for categorizing said test image. A processor of an electronic device typically controls the image manager to effectively perform the image categorization procedure.

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

Abstract: A technology for creating an arbitrary viewpoint image with high quality based on pixel value information as seen from a single viewpoint and real spatial positional information has not been known. A hidden point is extracted based on a relative positional relationship between a single viewpoint closed surface projection point for arbitrary measurement point and neighboring projection point, a relative positional relationship between other viewpoint closed surface projection point for arbitrary measurement point and neighboring projection point, distance from arbitrary measurement point to another viewpoint, and distance from measurement point projected onto neighboring projection point on the other viewpoint closed surface to another viewpoint.

Abstract: A computer implemented method for point matching comprising providing a pair of images captured, selecting first and second sets of interest points from the images; constructing a control network of super points for each set of interest points; assigning a position, with respect to the closest network control point of each control network, to other interest points on the images; locating conjugate points for each other interest point of each set based on its assigned position; and adding the conjugate points to the control network.

Abstract: A hardware coprocessor architecture calculates the Difference-of-Gaussian (DoG) pyramid of an input image and extracts from this the interest points to be used in several image detection algorithms. Advantages of the architecture include the possibility to process the image by stripes, namely by blocks having one dimension coincident with the input image width, in the absence of an input frame buffer and the possibility to avoid RAM memory. The coprocessor is suitable to be tightly coupled with raw image sources like sensors.

Abstract: A PSF cost function used for determining a PSF kernel (438) of at least a portion of an image (16) includes (i) a first fidelity term having a first direction derivative, (ii) a first fidelity term weight for the first fidelity term, (iii) a second fidelity term having a second direction derivative, and (iv) a second fidelity term weight for the second fidelity term. Another PSF cost function for determining a PSF kernel (638) includes (i) a first fidelity term with a first derivative in a dominant edge direction (d), and (ii) a second fidelity term having a second derivative in a perpendicular direction(p) to the dominant edge direction.

Abstract: A method of determining a contour segment for an object in an image captured by a camera is disclosed. A plurality of corner points located on a part of an object in the image is determined. Pairs of corner points are selected from the determined corner points. A path for at least one of the pairs of corner points is determined, the path connecting the corner points of the at least one pair of corner points along the boundary of the object. The contour segment is determined for the object using the determined path.

Abstract: There is provided an information processing apparatus including an image acquisition unit configured to acquire images captured from a plurality of observation points for a predetermined object, a feature point extraction unit configured to extract a feature point in each of the images acquired by the image acquisition unit, a correspondence relationship acquisition unit configured to acquire a correspondence relationship of the feature points based on images from among adjacent observation points, and an information presentation unit configured to quantitatively present information about the correspondence relationship acquired by the correspondence relationship acquisition unit.

Abstract: A method for comparing a first image with a second image. The method identifies first keypoints in the first image and second keypoints in the second image and associates each first keypoint with a corresponding second keypoint to form a corresponding keypoint match. For each pair of first keypoints, the method further calculates the distance therebetween for obtaining a corresponding first length. Similarly, for each pair of second keypoints, the method calculates the distance therebetween for obtaining a corresponding second length. The method further calculates a plurality of distance ratios; each distance ratio is based on a length ratio between a selected one between a first length and a second length and a corresponding selected one between a second length and a first length, respectively.

Abstract: According to an embodiment, a method for filtering descriptors for visual object recognition is provided. The method includes identifying false positive descriptors having a local match confidence that exceeds a predetermined threshold and a global image match confidence that is less than a second threshold. The method also includes training at least one classifier to discriminate between the false positive descriptors and other descriptors. The method further includes filtering feature point matches using the at least one classifier. According to another embodiment, the filtering step may further include removing one or more feature point matches from a result set. According to a further embodiment, a system for filtering feature point matches for visual object recognition is provided. The system includes a hard false positive identifier, a classifier trainer and a hard false positive filter.

Abstract: An information processing device and method can detect facial feature point positions with high accuracy. The information processing device includes: a facial image input unit receiving a facial image; a facial feature point reliability generation unit generating, based on a plurality of classifiers for computing the suitability of feature points of a face, a reliability image indicating the suitability for each feature point from the facial image; a facial feature point candidate position computation unit obtaining a candidate position of the feature point in the facial image based on the reliability image; and a facial shape model conformity computation unit determining a feature point that satisfies conditions based on a position corresponding to each of the feature points of one facial shape model selected from a plurality of statistically generated facial shape models and the candidate position of the feature point, and calculating a conformity to the facial shape model.