Abstract: According to the disclosure of the present document, chroma quantization parameter offset-related information may be signaled via a palette coding syntax, and index information about a chroma quantization parameter offset list may be efficiently parsed/signaled on the basis of information about the number of entries within the chroma quantization parameter offset list. Accordingly, bits that need to be signaled for video/image coding can be reduced, and coding efficiency can be improved.

Abstract: The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

Abstract: A system for obtaining a multispectral image of a scene includes a first light source, a second light source, at least one imaging sensor, and a controller. The first light source emits light in a first wavelength range. The second light source emits light in a second wavelength range. The at least one imaging sensor senses light in the first wavelength range reflected off of the scene during a first illumination sensing period and senses light in the second wavelength range reflected off of the scene during a second illumination sensing period. The controller is electrically coupled to the at least one imaging sensor. The controller interprets signals received from the at least one imaging sensor as imaging data, stores the imaging data, and analyzes the imaging data with regard to multiple dimensions. The first illumination sensing period and the second illumination sensing period are discrete time periods.

Abstract: Methods, systems, and devices for improved video throughput using deep learning video coding are described. A device may receive a bitstream including a set of video frames. The device may batch the set of video frames into a first subset of video frames and a second subset of video frames based on a change in a reference scene associated with the set of video frames. The device may select a mode of operation for a neural processing unit of the device based on the batching. The device may generate a set of video packets including the first subset of video frames, the second subset of video frames, or both, based on the neural processing unit and the selected mode of operation.

Abstract: A cap mountable on and detachable from an intraoral scanner including a housing having an opening for connecting to at least a part of the intraoral scanner, a measurement window provided in the housing and located opposite to the opening, and a mirror that reflects light from the measurement window toward the opening. The housing has a longitudinal length shorter than that of a probe defining a depth-of-field, of the intraoral scanner extending from the measurement window to a predetermined extent.

Abstract: A vehicle position estimation device is configured to acquire a distance from a road edge to a subject vehicle using at least one of an imaging device or a distance measuring sensor, acquire position information of lane boundary lines detected by analyzing the images captured by the imaging device; acquire map information including a lane quantity of a traveling road of the subject vehicle from a map storage, calculate, as a roadside area width, a lateral direction distance between an outermost detection line, which is an outermost boundary line among the detected boundary lines, and the road edge, and specify a traveling lane of the subject vehicle based on (i) the distance from the road edge to the subject vehicle, (ii) the roadside area width, and (iii) the lane quantity included in the acquired map information.

Abstract: In some implementations, an optical component of a microscope may capture an image of a profile of a ferrule and a connector of an optical fiber based on the ferrule being received by a first opening of a first connector adapter of the microscope. A mechanical axis of the ferrule may be orthogonal to an optical path from a camera of the microscope to the ferrule when the ferrule is received by the first opening. One or more processors associated with the microscope may process the image to determine a measurement of a chamfer of the ferrule. The optical component may capture an image of an endface of the ferrule based on the ferrule being received by a second opening of a second connector adapter. The mechanical axis of the ferrule may be axially aligned with the optical path when the ferrule is received by the second opening.

Abstract: The present disclosure provides a vehicle positioning device for vehicle vision calibration, a positioning adjustment method, and a positioning adjustment system. The vehicle positioning device includes a base, a chassis, a translation mechanism and a rotation mechanism. A to-be-calibrated vehicle is placed onto the chassis, and the chassis is provided with a stopper. The translation mechanism is arranged between the chassis and the base, and configured to drive the chassis to move in an X-axis direction in a three-dimensional coordinate system relative to the base in accordance with a translation control instruction. The rotation mechanism is arranged between the chassis and the base, and configured to drive the chassis to rotate about a Z-axis in the three-dimensional coordinate system relative to the base in accordance with a rotation control instruction.

Abstract: A driver monitoring system of a vehicle includes: a camera configured to capture an image of a driver on a driver's seat within a passenger cabin of the vehicle; a gaze module configured to determine a gaze vector based on a direction of pupils of the driver in the image; an area module configured to determine an area on a vertical plane in front of the driver based on a road in front of the vehicle; a location module configured to determine a location on the vertical plane where the gaze vector intersects the vertical plane; and a monitor module that determines whether the location on the vertical plane where the gaze vector intersects the vertical plane is within the area on the vertical plane.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: There is includes a method and apparatus comprising computer code configured to cause a hardware processor or processors to perform intra prediction among a plurality of reference lines, to set a plurality of intra prediction modes for a zero reference line nearest to a current block of the intra prediction among non-zero reference lines, and to set one or more most probable modes for one of the non-zero reference lines.

Abstract: An information processing device (100) includes a calibration execution unit (195) that performs calibration between two or more sensors which are attached at different positions and of which visual field regions at least partially overlap each other. The calibration execution unit (195) performs the calibration between the sensors in a case where a feature point that enables calibration of each of the two or more sensors is acquirable in a region in which visual fields of the two or more sensors overlap each other.

Abstract: A system and method of using a tool assembly is provided. The system includes a body, a first camera and a second camera fixed to the body, and a controller. The controller is configured to receive data indicative of images of a reference feature from the first camera, determine data indicative of a first spatial position of the first camera based at least in part on the received data indicative of the images of the reference feature, and determine data indicative of a second spatial position of the second camera based on the first spatial position, a known spatial relationship between the first location and the second location, or both. Further, the controller may be configured to receive data indicative of images of a target feature using the second camera, derive dimensions of the target feature based on the images, and generate a three-dimensional representation of the target feature.

Abstract: A displacement meter for suppressing errors occurring in sub-pixel estimation, comprising: alight source that illuminates an object; an image pickup unit for receiving diffused-reflected light from the object; and a calculation unit for calculating the a displacement amount of the object by using sub-pixel estimation and the cross-correlation function between reference image data and measurement image data, wherein an image acquired during a predetermined timing from the imaging unit serves as the reference image data and an image acquired during the next timing serves as the measurement image data, generates a correction displacement amount by subtracting a correction value from the displacement amount, sets the most recent measurement image data as the reference image data and sets a predetermined initial value as the correction value if the displacement amount meets a predetermined condition, and else sets the displacement amount obtained most recently as the correction value.

Abstract: A system for cosmetic inspection of a test object is disclosed that includes a movable platform for receiving a test object. The movable platform is capable of positioning the test object within a dome. A plurality of cameras arranged oriented to capture different views of a plurality of surfaces of the test object. A plurality lights arranged are outside the dome, the plurality of lights selectively enabled or disabled according to which of the plurality of surfaces of the test object is to be captured.

Abstract: An image decoding method according to the present document includes obtaining motion prediction information for a current block from a bitstream, generating an affine MVP candidate list for the current block, deriving CPMVPs for CPs of the current block based on the affine MVP candidate list, deriving CPMVDs for the CPs of the current block based on the motion prediction information, deriving CPMVs for the CPs of the current block based on the CPMVPs and the CPMVDs, and deriving prediction samples for the current block based on the CPMVs.

Abstract: This disclosure describes techniques for capturing still images during video streaming to identify patterns in a region-of-interest on the captured still images. The video streaming may be performed by an imaging device that includes or is communicatively connected to one or more sensors (e.g., radar, light sensor, etc.) The one or more sensors may be configured to perform data measurements such as vehicle speed measurements, light intensity measurements, and/or the like. In one example, during the video streaming, the data measurement may be compared with a corresponding threshold. In this example, the imaging device may be triggered to capture still images of the surrounding area based on the comparison between the data measurement and the corresponding threshold. Thereafter, the still images may be processed to identify the region-of-interest on the still images.

Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representati

Abstract: A method, computer program, and computer system is provided for coding video data. Video data having a coding tree unit size is received. The coding tree unit size associated with the video data is signaled by setting two or more flags. The video data is encoded/decoded based on the flags corresponding to the signaled coding tree unit size.

Abstract: System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.