Abstract: A camera control system is provided for controlling operation of a camera mounted on a vehicle. The system includes a processor and a memory communicably coupled to the processor. The memory stores a camera control module configured to compare a recognition confidence level associated with a feature to a predetermined threshold and, responsive to the recognition confidence level being below the predetermined threshold and using location information associated with the feature, control operation of the camera to capture at least one image of the feature during movement of the vehicle.

Abstract: The disclosure extends to methods, systems, and computer program products for widening dynamic range within an image in a light deficient environment.

Abstract: A method of video processing includes performing a conversion between a video and a bitstream of the video, wherein the bitstream includes one or more output layer sets (OLSs) each including one or more video layers, wherein the bitstream conforms to a format rule. The format rule specifies whether or how a first syntax element indicating whether a first syntax structure descriptive of parameters of a hypothetical reference decoder (HRD) used for the conversion is included in a video parameter set (VPS) of the bitstream.

Abstract: A system comprises one or more processors and one or more storage devices. The system is configured to receive first image data of a first image of an object from a first image sensor and receive second image data of a fluorescence image of the object from a second image sensor. Further, the system is configured to process the first image data and the second image data and generate combined image data of an output image of the object in a linear color space based on the processed first image data and the processed second image data. Additionally, the system is configured to convert the combined image data to a non-linear color space representation of the output image.

Abstract: Endoscopic image analysis, endoscopic procedure analysis, and/or component control systems, methods and techniques are disclosed that can analyze images of an endoscopic system and/or affect an endoscopic system to enhance operation, user and patient experience, and usability of image data and other case data.

Abstract: A video encoding apparatus is a video encoding apparatus for subjecting a video image to motion compensated prediction coding, comprising an acquisition module to acquire available blocks of blocks having motion vectors from encoded blocks adjacent to a to-be-encoded block and number of the available blocks, an acquisition/selection module to select one selection block from the encoded available blocks, a selection information encoder to encode selection information specifying the selection block using a code table corresponding to the number of available blocks, and an image encoder to subject the to-be-encoded block to motion compensated prediction coding using a motion vector of the selection block.

Abstract: To improve a coding efficiency. There are included a PU level search unit configured to search for a motion vector for each prediction block by using a matching process. and a sub-block level search unit configured to search for a motion vector of each of sub-blocks in the prediction block, wherein a precision of a local search by the PU level search unit is lower than a precision of a local search by the sub-block level search unit.

Abstract: A speed estimation system includes: a detection module configured to determine bounding boxes of an object moving on a surface in images, respectively, captured using a camera; a solver module configured to, based on the bounding boxes, determine a homography of the surface by solving an optimization problem, where the solver module is not trained; and a speed module configured to, using the homography, determine a speed that the object is moving on the surface.

Abstract: A method of generating a color image using a monochromatic image sensor. The method includes sequentially illuminating a surface in a plurality of colors, one color at a time. The monochromatic image sensor captures a plurality of image frames of the surface based on the plurality of colors. The plurality of image frames are identified, and at least one feature in the target of the plurality of image frames is highlighted. Color intensities of the plurality of image frames are normalized. A color intensity map of the target for each of the plurality of image frames is generated. A correlation score is determined by comparing each color intensity map of the plurality of image frames. The color image is generated based on the correlation score.

Abstract: An image decoding device (31) includes a transform coefficient decoding unit (311) configured to decode a transform coefficient for a transform tree included in a coding unit. In the transform tree, the transform coefficient decoding unit splits a transform unit corresponding to luminance and then decodes the transform coefficient related to the luminance, and does not split the transform unit corresponding to chrominance and decodes the transform coefficient related to the chrominance.

Abstract: A method of video coding at a video coding device includes performing a deformable convolution through a deformable convolutional deep neural network (DNN) to generate one or more first feature maps based on a set of one or more previously reconstructed reference frames, generating a predicted frame based on the one or more first feature maps, and reconstructing a current frame based on the predicted frame. In an embodiment, a set of one or more second feature maps corresponding to the one or more previously reconstructed reference frames can be generated based on a feature extraction DNN. One or more offset maps corresponding to the one or more second feature maps can be generated, respectively, using an offset generation DNN.

Abstract: Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced images in multi-camera systems, e.g., by using information from images captured by cameras with different properties in terms of optics and/or sensors. In one embodiment, the techniques comprise: obtaining a first image from a first image capture device, wherein the first image has a first field of view (FOV) and a first set of quality characteristics; obtaining a second image from a second image capture device, wherein the second image has a second FOV and a second set of quality characteristics, and wherein the second FOV partially overlaps the first FOV; obtaining a neural network that produces a modified second image having a modified second set of quality characteristics determined by the neural network attempting to match the first set of quality characteristics; and generating an output image based, at least in part, on the modified second image.

Abstract: A three-dimensional viewing includes a pair of digital image sensors in communication with a pair of digital displays and controlled by a pair of processors to render three-dimensional images of samples on the displays.

Abstract: A vehicular display system includes an electronic control unit (ECU) and a plurality of cameras disposed at the vehicle. The cameras capture image data and provide captured image data to the ECU. The system includes a video display screen and a video mirror display screen that are operable to display video images derived from video images provided by the ECU. When the vehicle is traveling forward at a speed below a threshold speed, the ECU generates rearward view video images derived from image data captured by a rearward viewing camera and provides the rearward view video images to the video display screen. When the vehicle is traveling forward at a speed at or above the threshold speed, the ECU generates rearward view video images derived from image data captured by a rearward viewing camera and provides the rearward view video images to the video mirror display screen.

Abstract: The technology is directed towards image compression and decompression, including for 3D images. A sender device obtains lower resolution sub-images of an original image, captured from different angles. The sender device uses different neural network candidate level data and groups of candidate sub-images to determine selected neural network level data and sub-images that when combined, reconstruct the original image that satisfies a function of quality loss data, for sending to a receiver device. The receiver device receives the neural network level data, the group of sub-images and the reconstruction data, and reconstructs a three-dimensional representation of the original image using the neural network level and the group of sub-images arranged based on the reconstruction data.

Abstract: An inspection apparatus includes a specimen stage, one or more imaging devices and a set of lights, all controllable by a control system. By translating or rotating the one or more imaging devices or specimen stage, the inspection apparatus can capture a first image of the specimen that includes a first imaging artifact to a first side of a reference point and then capture a second image of the specimen that includes a second imaging artifact to a second side of the reference point. The first and second imaging artifacts can be cropped from the first image and the second image respectively, and the first image and the second image can be digitally stitched together to generate a composite image of the specimen that lacks the first and second imaging artifacts.

Abstract: There is provide a device including: a first storage unit configured to store, when an object moves between separate image capturing areas which are captured by a plurality of surveillance cameras, a plurality of movement histories of the object between image data respectively captured by the surveillance cameras; an identification unit configured to identify, among the plurality of surveillance cameras, one surveillance camera that has captured a target object to track, and an image capturing time, according to an operation of an operator; and an estimation unit configured to estimate at least one other surveillance camera that is different from the one surveillance camera and that captures the target object, among the plurality of surveillance cameras, and an estimated time when the other surveillance camera captures the target object, based on the movement history and an identification result obtained by the identification unit.

Abstract: The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.

Abstract: An example device for processing point cloud data includes a memory configured to store the point cloud data and one or more processors implemented in circuitry and coupled to the memory. The one or more processors are configured to count a number of edges of a cube of point cloud data comprising a vertex. The one or more processors are configured to set a variable based on a total of the counting. The one or more processors are also configured to process the point cloud data based on the variable.

Abstract: A pipeline inspection system may include a cable that may be configured to be directed into a conduit. The pipeline inspection system may further include a camera disposed on a distal end of the cable and operable to capture an image. The pipeline inspection system may further include a monitor including a display configured to display an image. The pipeline inspection system may further include an electronic processor that may be configured to receive a user input indicating a characteristic of the conduit. The electronic processor may be further configured to determine an image setting of the display based on the characteristic of the conduit. The electronic processor may be further configured to control the monitor to display the image on the display according to the image setting.