Abstract: A method, system, device, and/or a non-transitory computer readable medium to provide a customized application associated with a television experience based on the recognition of users located in front of a television display and in the field of view of a camera. The method may include performing an initializing operation, the initializing operation including enrolling a plurality of users in a database of a computer system, acquiring a wide image using the camera and scanning the wide image for biometric information; and performing an identification operation requested by the application including, acquiring a second wide image with the camera, extracting an active area from the second wide image, storing the extracted active area as a second fast scanning area image, and extracting the biometric data of a face appearing in the second fast scanning area image.

Abstract: The disclosure relates to a system and method for image reconstruction. The method may include the steps of: obtaining raw data corresponding to radiation rays within a volume, determining a radiation ray passing a plurality of voxels, grouping the voxels into a plurality of subsets such that at least some subset of voxels are sequentially loaded into a memory, and performing a calculation relating to the sequentially loaded voxels. The radiation ray may be determined based on the raw data. The calculation may be performed by a plurality of processing threads in a parallel hardware architecture. A processing thread may correspond to a subset of voxels.

Abstract: A system and method to determine a flow direction automatically so that counter-flow motion may be detected without user calibration is disclosed. The system and method utilize information available from existing video compression algorithms to sense motion and determine a flow direction. Once a flow direction for an area is determined, the system and method can detect counter-flow motion using compressed video without any decompression, thereby improving the speed of detection and simplifying the computational complexity. These improvements facilitate counter-flow alarms and expand the information available to a user monitoring the video.

Abstract: A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.

Abstract: Computer-implemented systems and methods for evaluating a performance are provided. Motion of a user in a performance is detected using a motion capture device. Data collected by the motion capture device is processed with a processing system to identify occurrences of first and second types of actions by the user. The data collected by the motion capture device is processed with the processing system to determine values indicative of amounts of time between the occurrences. A non-verbal feature of the performance is determined based on the identified occurrences and the values. A score for the performance is generated using the processing system by applying a computer scoring model to the non-verbal feature.

Abstract: A method for learning parameters used to recognize characters included in a text in a scene text image of training set is provided. The method includes steps of: (a) a training apparatus generating each feature vector corresponding to each of the segmented character images; (b) the training apparatus processing feature vectors ci+j of neighboring character images to thereby generate a support vector to be used for a recognition of a specific character image; (c) the training apparatus obtaining a merged vector by executing a computation with the support vector and a feature vector ci of the specific character image; and (d) the training apparatus (i) performing a classification of the specific character image as a letter included in a predetermined set of letters by referring to the merged vector; and (ii) adjusting the parameters by referring to a result of the classification.

Abstract: Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.

Abstract: Techniques and systems are provided for processing video data. For example, techniques and systems are provided for maintaining blob trackers for one or more video frames. A blob tracker can be associated with a blob generated for a video frame. The blob includes pixels of at least a portion of one or more foreground objects in the video frame. The blob tracker can be determined to be a first type of tracker or a second type of tracker. A first type of tracker has a first bounding box and a second bounding box with an overlapping ratio greater than an alignment threshold for the first type of tracker. A second type of tracker has an irregular size change or an irregular motion change over a threshold duration. The blob tracker can be removed from the plurality of blob trackers maintained for the one or more video frames when the blob tracker is the first type of tracker or the second type of tracker.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one ore more objects of interest from the classified pixels.

Abstract: During an intraoral scan session, a processing device receives a plurality of intraoral images of a dental site. The processing device determines that a historical template of the dental site is existent and registers a first intraoral image of the plurality of intraoral images to the historical template at a first region of the dental site depicted in the historical template. The processing device may register one or more remaining intraoral images of the plurality of intraoral images to the historical template at a second region of the dental site depicted in the historical template. The first region may be separated from the second region.

Abstract: Techniques for processing ultrasound data. The techniques include using at least one computer hardware processor to perform obtaining ultrasound data generated based, at least in part, on one or more ultrasound signals from an imaged region of a subject; calculating shadow intensity data corresponding to the ultrasound data; generating, based at least in part on the shadow intensity data and at least one bone separation parameter, an indication of bone presence in the imaged region, generating, based at least in part on the shadow intensity data and at least one tissue separation parameter different from the at least one bone separation parameter, an indication of tissue presence in the imaged region; and generating an ultrasound image of the subject at least in part by combining the indication of bone presence and the indication of tissue presence.

Abstract: A method for dismount detection in low-resolution UAV imagery, comprising providing an input image, processing a greyscale distribution of the input image, determining a rough classification in the input image based on the grayscale distribution, determining the optimal parameters based on the rough classification, estimating one or more potential dismount locations, applying an area filter to the one or more potential dismount locations, removing undesired locations from the one or more potential dismount locations, applying one or more secondary filters to the resulting one or more potential dismount locations, assigning a probability to the one or more potential dismount locations, and assessing desirability of the one or more potential dismount locations.

Abstract: A method and apparatus for determining a building/urban profile image location includes extracting a building/urban profile from an obtained image and comparing the image to a database of building/urban profiles. The database may be created by obtaining point cloud data sets scanned building/urban profiles, converting the obtained point cloud data sets to a corresponding 3D surface model, and creating the database of stored building/urban profiles viewed from multiple locations within each of the 3D surface models. By comparing the extracted building/urban profile image with the stored building/urban profiles contained within the database and finding a match the location from which the extracted building/urban profile image was taken can be determined.

Abstract: Various aspects of a system and a method for background subtraction in video content are disclosed herein. The system includes an electronic device, which generates an optical flow image of an image frame from a sequence of image frames. A first distribution is determined based on pixel values of a plurality of pixels of the generated optical flow image. A second distribution is determined based on pixel values of a set of pixels of the plurality of pixels of the generated optical flow image. The set of pixels correspond to pixels that touch one or more borders of the image frame. A third distribution is further determined based on the determined first distribution and the determined second distribution. One or more peak values corresponding to one or more background regions in the generated optical flow image, are determined within a specified threshold range in the determined third distribution.

Abstract: An image processing apparatus and related method. The apparatus (PP) comprises an input port (IN), a classifier (CLS) and an output port (OUT). The input port is capable of receiving an image of an object acquired at a field of view (FoV) by an imager (USP). The image records a pose of the object corresponding to the imager's field of view (FoV). The classifier (CLA) is configured to use a geometric model of the object to determine, from a collection of pre-defined candidate poses, the pose of the object as recorded in the image. The output port (OUT) is configured to output pose parameters descriptive of the determined pose.

Abstract: The present disclosures provide methods of optical character recognition for extracting information from a patterned document, which have at least static element and at least one information field. Related computer systems and computer-readable non-transitory storage media are also disclosed.

Abstract: In order to provide an arithmetic device, an X-ray CT apparatus, and an image reconstruction method, capable of reducing processing time while maintaining a noise reduction effect, in a successive approximation image reconstruction method (separable paraboloidal surrogate (SPS) method) of the related art, updated images are forward-projected, whenever images are repeatedly updated, a difference between forward projection data and original object projection data is back-projected so that a difference image is obtained, and a forward projection process and a back projection process are repeatedly performed, but, in the present invention, a forward projection process and a back projection process requiring calculation time are replaced with a process requiring a relatively small calculation amount, such as a difference between an updated image and a reference image, and, as a result, it is possible to considerably reduce a calculation amount in a successive approximation image reconstruction process and to reduc

Abstract: An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to acquire correction information on a luminance value of a contrast image, based on concentration of a contrast material and thickness of the contrast material in a projection direction in a device that is inserted into a blood vessel, in the contrast image. The processing circuitry is configured to correct the contrast image using the correction information.

Abstract: A device may receive one or more images captured by an image capture system. The one or more images may depict one or more objects. The device may process the one or more images using one or more image processing techniques. The device may identify the one or more objects based on processing the one or more images. The device may identify a context of the one or more images based on the one or more objects depicted in the one or more images. The device may determine whether the one or more objects contribute to a value of one or more metrics associated with the context. The device may perform an action based on the value of the one or more metrics.

Abstract: The commodity recognition apparatus displays a frame for surrounding a commodity in an image captured by an image capturing module. Then the commodity recognition apparatus recognizes a candidate of the commodity imaged in the frame according to the feature amount of the image in the area surrounded by the frame, and outputs information of a highest ranked candidate commodity. If a change instruction for the candidate is received, the commodity recognition apparatus outputs information of the commodity other than the highest ranked candidate selected from the candidates.