Abstract: The present invention relates to an image encoding/decoding method and apparatus. An image encoding method according to the present invention may comprise generating a transform block by performing at least one of transform and quantization; grouping at least one coefficient included in the transform block into at least one coefficient group (CG); scanning at least one coefficient included in the coefficient group; and encoding the at least one coefficient.

Abstract: Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

Abstract: An eye tracking system comprises at least one illuminator and at least one image sensor configured to produce an image of an eye of a user, the image including illuminator light reflected from the eye of a user. A Fresnel lens is positioned between the image sensor and the eye of the user, through which the image sensor views the eye. Processing circuitry receives an image from the image sensor, identifies glints in the image, assigns an angular position to each glint based on an angular relationship between each glint and a centre of the Fresnel lens and determines how many glints have the same angular position. Glints are classified as false glints if more than a predetermined number of glints have the same angular position.

Abstract: Techniques related to video coding include multiple channel video coding with cross-channel referencing.

Abstract: There are provided an apparatus and a method for generating a panorama image. The apparatus includes: a Region Of Interest (ROI) selecting block for receiving a plurality of images, and outputting ROI images by selecting an ROI of each of the plurality of images; a High Dynamic Range (HDR) processing block for performing HDR processing on the ROI images; and a panorama image generating block for generating a panorama image by stitching the plurality of images using a matching coordinate of the ROI images on which the HDR processing is performed. The HDR processing block performs the HDR processing on the panorama image.

Abstract: Techniques are described for passive three-dimensional image sensing based on referential image blurring. For example, a filter mask is integrated with a lens assembly to provide one or more normal imaging bandpass (NIB) regions and one or more reference imaging bandpass (RIB) regions, the regions being optically distinguishable and corresponding to different focal lengths and/or different focal paths. As light rays from a scene object pass through the different regions of the filter mask, a sensor can detect first and second images responsive to those light rays focused through the NIB region and the RIB region, respectively (according to their respective focal lengths and/or respective focal paths). An amount of blurring between the images can be measured and correlated to an object distance for the scene object. Some embodiments project additional reference illumination to enhance blurring detection in the form of reference illumination flooding and/or spotted illumination.

Abstract: Techniques are described for passive three-dimensional image sensing based on referential image blurring. For example, a filter mask is integrated with a lens assembly to provide one or more normal imaging bandpass (NIB) regions and one or more reference imaging bandpass (RIB) regions, the regions being optically distinguishable and corresponding to different focal lengths and/or different focal paths. As light rays from a scene object pass through the different regions of the filter mask, a sensor can detect first and second images responsive to those light rays focused through the NIB region and the RIB region, respectively (according to their respective focal lengths and/or respective focal paths). An amount of blurring between the images can be measured and correlated to an object distance for the scene object. Some embodiments project additional reference illumination to enhance blurring detection in the form of reference illumination flooding and/or spotted illumination.

Abstract: A method includes performing a conversion between a video comprising a video unit and a bitstream of the video, wherein the bitstream conforms to a format rule, and wherein the format rule specifies that a first syntax element indicating whether an in-loop filtering operation is performed across tile boundaries is selectively included in the bitstream based on whether or how the video unit is partitioned into tiles.

Abstract: Techniques are described for expanding and/or improving the Advanced Television Systems Committee (ATSC) 3.0 television protocol in robustly delivering the next generation broadcast television services. Telepresence is provided through over-the-air (OTA) virtual reality (VR) broadcast streams.

Abstract: The system described herein includes a vehicle comprising one or more imaging devices configured to capture one or more images, and a first mobile computing device communicatively coupled to the vehicle. The first mobile computing device includes one or more processors; one or more memory modules; and machine readable and executable instructions that, when executed by the one or more processors, cause the first mobile computing device to: receive the one or more images including an external object from the vehicle; process the image to determine an identification of the external object; and transmit information about the identification of the external object to a remote server or the vehicle.

Abstract: Methods and apparatuses for acquiring display image sequence, rotary stereoscopic display devices and storage media are provided. A method includes: determining voxels in a 3D display space by multiple partitioning of the 3D display space in a cylindrical coordinate system using a predetermined partitioning mode, where the predetermined partitioning mode includes non-uniform partitioning in a radius direction of the cylindrical coordinate system, and space bounding boxes resulting from a same partitioning have a same volume; determining a cross-sectional profile of a 3D object to be displayed on a display screen at each of phase positions; determining a cross-sectional image based on voxels and the cross-sectional profile at each of the phase positions; and acquiring a display image sequence by arranging the cross-sectional images according to rotation angles of the display screen.

Abstract: A method and apparatus for identifying an Access Unit (AU) boundary in a coded video bitstream comprising: correlating information from at least one field from each of at least two Network Abstraction Layer (NAL) units.

Abstract: An electronic device for a vehicle for augmenting image data of a passive optical sensor. The electronic device is configured to: receive first image data of a passive optical sensor, the first image data comprising a plurality of pixels in an image plane, receive second data of an active sensor, the passive optical sensor and the active sensor sensing the same scene outside the vehicle, the active sensor including a plurality of scan areas distributed over the scene, the second data including measurement values at returned scan areas, identify at least one cluster based on the distribution of returned scan areas, project the cluster onto the image plane, identify pixels which match with the projected cluster in the image plane, and associate the identified pixels with second data of the matching cluster. Further relates to a system and a method.

Abstract: An example method of decoding video data includes obtaining, from a coded video bitstream and for a current block of the video data, an indication of an intra-prediction mode that identifies an initial predictive block; filtering, in parallel, samples in a current line of a plurality of lines of the initial predictive block based on filtered values of samples in a preceding line of the plurality of lines and unfiltered values of samples in the current line to generate filtered values for samples for the current line; and reconstructing, using intra prediction, values of samples of the current block based on the filtered values of the samples of the current initial predictive block and residual data for the current block that represents a difference between the filtered values of the samples of the current initial predictive block and the values of samples of the current block.

Abstract: An automotive sensor integration module including a plurality of sensors differing from each other in at least one of a sensing cycle or an output data format, and a signal processor for selecting a plurality of weights for each of the plurality of sensors on the basis of external environment data, and applying the plurality of weights to a plurality of detection data output from the plurality of sensors respectively to generate a plurality of weighted data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a sensor activation and correlation system. One of the methods includes obtaining information describing a flight plan for implementation. A trigger associated with activating a camera included in an unmanned aerial vehicle (UAV) is determined to be satisfied. Information indicating that the camera is to capture an image is provided to the camera. A first timestamp associated with the image is obtained and stored. Information indicating that the image was captured is received and associated with the obtained first timestamp. Information indicating that the camera captured the image is provided to a user device in communication with the UAV.

Abstract: An image capturing support apparatus 1 includes: a detection unit 2 configured to detect facial images that have facial areas corresponding to a face, from a plurality of captured images, respectively; a determination unit 3 configured to determine whether or not the amount of light is insufficient for the detected facial images, using the detected facial images; and a superimposition unit 4 configured to, when the amount of light is insufficient, adjust the size of each of the facial images, and superimpose the facial areas of the adjusted facial images on each other.

Abstract: A process to partition a video feed to segment live player activity includes receiving, on a first recurring basis, a transmission of a central video feed from a first camera. The central video feed is calibrated against a spatial region represented in at least two dimensions that is encompassed by the central video feed. The process includes receiving, on a second recurring basis, a respective time-stamped position information from each tracking device in a plurality of tracking devices. Each tracking device is worn by a corresponding subject on the spatial region and transmits positional information that describes a time-stamped position of the corresponding subject in the spatial region. The process uses the received information and the calibration to define a first sub-view of the central video feed associated with a first subject. The first sub-view comprises a corresponding sub-frame associated with the first subject.

Abstract: A near-range camera assembly for a vehicle is provided. The near-range camera assembly includes a LIDAR mount couplable to a vehicle. The LIDAR mount includes a first surface and a second surface that opposes the first surface. The near-range camera assembly includes a LIDAR unit removably coupled to the first surface LIDAR mount. The near-range camera assembly further includes a camera and a camera housing for the camera. The camera housing is removably coupled to the second surface LIDAR mount.

Abstract: A system comprises an image sensor with a field of view of an exterior of a vehicle; a human machine interface (HMI) in the vehicle arranged to display images based on data from the image sensor; and a first computer that includes a processor and a memory. The first computer is programmed to provide data from the image sensor to a machine learning program that outputs a bounding box around an object; cause the bounding box around the object to be displayed via the HMI; and transmit object data including an inputted label identifying the object to a second computer that is remote from the vehicle.