Abstract: The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.

Abstract: A video surveillance system and computer assisted method stores video streams of numerous video cameras installed throughout a facility (e.g., a school, an airport, etc.) and batch searches those video streams for relevant video frames that may show an individual with certain predetermined facial features. In some examples, the search range is limited by a specified time window, by certain designated cameras, and/or by certain predetermined areas of the facility. From those video streams, a collection of video clips containing the relevant video frames are identified and batch exported for later analysis by video surveillance personnel. In some examples, the collected video clips are compiled into a single composite video with the video clips being arranged by time for convenient and efficient viewing. In addition or alternatively, in some examples, the video clips are stored as separate files in a directory for viewing the video clips individually.

Abstract: The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising obtaining a sequence of images, detecting one or more objects in one or more images of the sequence of images, determining a location for each detected object in the one or more images, determining a trajectory for each detected object based on a determined location for each respective detected object in two or more images of the sequence of images, determining a trajectory waypoint score for the trajectory of each detected object, determining a set of selected images based on an aggregation of trajectory waypoint scores for each detected object in each respective image, and outputting the set of selected images for presentation.

Abstract: A method for 3D auto-labeling of objects with predetermined structural and physical constraints includes identifying initial object-seeds for all frames from a given frame sequence of a scene. The method also includes refining each of the initial object-seeds over the 2D/3D data, while complying with the predetermined structural and physical constraints to auto-label 3D object vehicles within the scene. The method further includes linking the auto-label 3D object vehicles over time into trajectories while respecting the predetermined structural and physical constraints.

Abstract: In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.

Abstract: A circuit, system, and method for measuring or detecting pressure or force of a fingerprint on an array of electrodes is described. Pressure or force may be measured or detected using a processed image of the fingerprint, or by measurement of capacitance of deformed variable capacitors.

Abstract: An image processing device has: a change detecting unit configured to, based on a captured image in which an product to be monitored is shown, detect a change in display status of the product; and a display detecting unit configured to detect that the product is returned to a different place from a place where the product has been picked up based on the change in display status of the product detected by the change detecting unit and on a person included in the captured image.

Abstract: There is provided an apparatus (100) for determining a location of a personal care device on a user. The apparatus (100) comprises a processor (102) configured to detect a location of one or more parts of the body of a user, detect motions of at least one of the parts of the body of the user induced by the personal care device when the personal care device is in use and determine the location of the personal care device on the user based on the detected motions of the at least one part of the body of the user in relation to the detected location of the at least one part of the body of the user. The processor (102) is configured to acquire images of the user from an imaging sensor (108) directed at the user, and detect the location of the one or more parts of the body of a user and the motions of the at least one part of the body of the user induced by the personal care device from the acquired images of the user.

Abstract: A method for improving geometry-based monocular structure from motion (SfM) by exploiting depth maps predicted by convolutional neural networks (CNNs) is presented. The method includes capturing a sequence of RGB images from an unlabeled monocular video stream obtained by a monocular camera, feeding the RGB images into a depth estimation/refinement module, outputting depth maps, feeding the depth maps and the RGB images to a pose estimation/refinement module, the depths maps and the RGB images collectively defining pseudo RGB-D images, outputting camera poses and point clouds, and constructing a 3D map of a surrounding environment displayed on a visualization device.

Abstract: A system for situational awareness monitoring within an environment, wherein the system includes one or more processing devices configured to receive an image stream including a plurality of captured images from each of a plurality of imaging devices, the plurality of imaging devices being configured to capture images of objects within the environment and at least some of the imaging devices being positioned within the environment to have at least partially overlapping fields of view, identify overlapping images in the different image streams, the overlapping images being images captured by imaging devices having overlapping fields of view, analyse the overlapping images to determine object locations within the environment, analyse changes in the object locations over time to determine object movements within the environment, compare the object movements to situational awareness rules and use results of the comparison to identify situational awareness events.

Abstract: Image processing techniques are described to receive bounding box information that describes a bounding box located around a detected object in an image, determine one or more positions of one or more reference points on the bounding box, determine, for each reference point, 3D world coordinates of a point of intersection of the reference point and the road surface, and assign the 3D world coordinates of the one or more reference points to a location of the detected object.

Abstract: A method includes determining, from a first image of a mouth, a first region of the first image comprising a representation of teeth; generating one or more parametric functions associated with tooth color based on color data from the first region; receiving image data comprising new contours of the mouth, wherein one or more of the teeth have a different position in the image data than in the first image; determining a second region comprising the teeth in the image data; and generating a new image based on the image data and the one or more parametric functions, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the one or more parametric functions to the second region of the image data.

Abstract: A face recognition technology that enables high-speed determination of erroneous detection of face regions and high-precision face recognition by means of a first face recognition processing unit of relatively low accuracy for detecting a face region of a person on each captured image captured by a monitor camera, and a second face recognition processing unit for performing face recognition of a person more accurately with respect to the detected face region transmitted from the first face recognition processing unit. The second face recognition. processing unit determines that the first face recognition processing unit has erroneously detected when the second face recognition processing unit is not able to recognize a face region on the detected face region transmitted from the first face recognition processing unit, and the first face recognition processing unit calculates and self-learns a barycentric coordinate of the detected face region as a point mask position. Thereafter, the first face recognition.

Abstract: A method for use in processing of image data stream is described. The method comprising: providing input data comprising at least one sequence of image data pieces indicative of a region of interest. Processing one or more image frames for detection of one or more foreground pixels regions. Collecting adjacent foreground pixels and determining one or more blobs associated with one or more suspected foreground objects in the region of interest Selecting one or more blobs and generating image portion indicative of one or more suspected foreground objects. And processing said one or more image portions for detection of one or more foreground objects and corresponding one or more object classes. For generating output data comprising class probability for one or more objects detected in one or more frame images of said image data stream.

Abstract: The present invention provides systems and methods for simultaneous localization and mapping from video with adversarial shape prior learning in real-time. For example, an unsupervised direct and dense SLAM may learn a geometry prior from data. Given a video sequence, a depth map of a target frame, as well as the target frame and the camera motions between the target frame and all the remaining frames may be output. Further, by fusing a camera motion estimate with a positional sensor's output, positional drift and the need for loop closure can be avoided.

Abstract: The present disclosure relates to a video processing method, and relates to the technical field of multimedia. The method can include: acquiring a source image from a source video, and obtaining a first object by recognizing the source image; adding an object identifier of the first object to the source image; and generating a target video based on the source image with a same object identifier.

Abstract: An objective of the present disclosure is to enable estimation of an attention target of communication parties even if there are a plurality of objects having the same appearance in a space. An attention target estimation device 10 according to the present disclosure acquires a first-person point-of-view video IMi captured from a perspective of a person and a line-of-sight position gi of a person when the first-person point-of-view video IMi is captured, identifies positions in a 3D space of objects 31, 32, and 33 extracted from the first-person point-of-view video IMi, and determines an object close to the line-of-sight position gi of the person among the objects 31, 32, and 33 included in the first-person point-of-view video IMi as the attention target of the person.

Abstract: The present invention provides a learning image generation apparatus (100) including: a background image acquisition unit (111) that acquires a background image; a background camera posture information acquisition unit (112) that acquires posture information of a background camera which generates a background image; an object continuous image acquisition unit (113) that acquires an object continuous image including an object; a synthesis position determination unit (121) that determines a synthesis position on the background image of the object included in each of a plurality of object still images included in the object continuous image based on the posture information of the background camera; and an image synthesis unit (131) that synthesizes the objects included in each of the plurality of object still images with the background image to generate a synthesis continuous image based on the background image, the object continuous image, the synthesis position.

Abstract: Systems, methods, and computer-readable storage media for digital fingerprinting of media content. The system does this by normalizing and pre-processing the media content into a standard format, then generating fingerprints for image, audio, and/or video aspects of the media. These fingerprints are mathematical vectors generated using one or more techniques, such as perceptual hashes coupled with machine learning. Once the fingerprints for new content is generated, the system can compare those fingerprints to fingerprints of known content to determine if the new content contains prohibited content.

Abstract: An investigation assist system includes a plurality of servers and an integration server communicatively connected to a terminal and the plurality of servers. In response to reception of a video captured by a plurality of cameras, each of the plurality of servers performs a video analysis of an object with respect to an incident, the plurality of servers processing different objects, respectively. Based on an input of a plurality of different object feature elements from the terminal, the integration server sends a search request for corresponding objects to the respective servers corresponding to the object feature elements, receives and integrates search results of the corresponding objects from the respective servers, and causes the terminal to display an integrated search result.