An information processing apparatus operable to perform computation processing in a neural network comprises a coefficient storage unit configured to store filter coefficients of the neural network, a feature storage unit configured to store feature data, a storage control unit configured to store in the coefficient storage unit a part of previously obtained feature data as template feature data, a convolution operation unit configured to compute new feature data by a convolution operation between feature data stored in the feature storage unit and filter coefficients stored in the coefficient storage unit, and compute, by a convolution operation between feature data stored in the feature storage unit and the template feature data stored in the coefficient storage unit, correlation data between the feature data stored in the feature storage unit and the template feature data.

An object detection device includes a processor. The processor is configured to apply a Hough transform to coordinate points on a UD map to detect a straight line having a predetermined length, and detect a target disparity corresponding to the detected straight line having the predetermined length as a disparity corresponding to an object parallel to a direction of travel of a stereo camera. In the UD map, a target disparity satisfying a predetermined condition among disparities acquired from a captured image is associated with coordinate points, each of the coordinate points having two-dimensional coordinates formed by a first direction and a direction corresponding to a magnitude of a disparity. The processor is configured to convert, in the Hough transform, a straight line passing through coordinate points associated with the target disparity and a predetermined range based on a vanishing point into a Hough space.

A method according to an aspect of the present disclosure includes combining pieces of first feature data and pieces of second feature data. Each of the pieces of first feature data is associated with first identification information related to a corresponding one or more of first constituents included in a first group and indicates a feature of the corresponding one or more of the first constituents. Each of the pieces of the second feature data corresponds to one of clusters in the second group. The second feature data includes statistic data associated with second identification information related to two or more of the second constituents included in a corresponding cluster. The statistic data has a statistic indicating a feature of two or more of the second constituents included in the corresponding cluster. The combining is performed based on the first identification information and the second identification information.

Disclosed is an electronic device for a vehicle, including a processor receiving first image data from a first camera, receiving second image data from a second camera, receiving first sensing data from a first lidar, generating a depth image based on the first image data and the second image data, and fusing the first sensing data for each of divided regions in the depth image.

An inspection apparatus including an image generation device which generates a second image corresponding to a first image, and a defect detection device which detects a defect in the second image. Each of the first and second image includes partial regions each including pixels. The defect detection device is configured to estimate a first value indicating a position difference between the first and second image for each of the partial regions, based on a luminance difference between the first and second image, estimate a second value indicating a reliability of the first value for each of the partial regions, and estimate a position difference between the first and second image for each of the pixels, based on the first and second value estimated for each of the partial regions.

An image processing method includes segmenting, by a cloud server and based on a zoom ratio of a high-definition image in a high-definition image library, a low-definition image of a small zoom ratio from a terminal to obtain a plurality of image blocks. For each image block, performing, by the cloud server, retrieval and matching in the high-definition image library to obtain one or more high-definition reference images, and then performing, by the cloud server, image quality improvement using an image enhancement network that is based on the high-definition reference images to obtain a plurality of high-definition image blocks. Finally, stitching, by the cloud server, the high-definition image blocks into a super-definition image with higher resolution and higher definition, and returning, by the cloud server, the super-definition image to the terminal.

According to one embodiment, an inspection apparatus includes an image generation device which generates a second image corresponding to a first image and a defect detection device which detects a defect in the second image with respect to the first image. The defect detection device is configured to extract a first partial region in which an amount of change of a luminance of the first image and an amount of change of a luminance of the second image have a correlation, and correct, in the first partial region, the luminance of the first image with respect to the luminance of the second image.

An augmented reality (AR) providing method for recognizing a context using a neural network includes acquiring, by processing circuitry, a video; analyzing, by the processing circuitry, the video and rendering the video to arrange a virtual object on a plane included in the video; determining whether a scene change is present in a current frame by comparing the current frame included in the video with a previous frame; determining a context recognition processing status for the video based on the determining of whether the scene change is present in the current frame; and in response to determining that the context recognition processing status is true, analyzing at least one of the video or a sensing value received from a sensor using the neural network and calculating at least one piece of context information, and generating additional content to which the context information is applied and providing the additional content.

In one aspect, it is provided a method including receiving an image of a target subject; determining a direction in response to the receipt of the image, the direction being one in which the target subject was likely to move during a time period in the past or is likely to move during a time period in the future; determining a target area within which another image of the target subject can be expected to appear based on the determined direction; and determining if a portion of a subsequent image is outside the determined target area to identify if the subsequent image is one relating to the target subject, wherein the subsequent image is one taken during the time period in the past or during the time period in the future.

An image scanning device scans one or plural document images of one or plural predetermined head pages, and a preview processing unit performs preview display of the document image(s) of the head page(s). After detecting a user operation to accept the document image on the preview display, the image scanning device scans a document image of a subsequent page to the head page(s), and the image processing unit performs the image process for the document image of the subsequent page. Further, the image processing unit performs a part or whole of the image process for the document image(s) of the head page(s) before the preview display. If a part of the image process is displayed before the preview display, the image processing unit performs a remaining part of the image process after detecting a user operation to accept the document image on the preview display.