An image matting method and apparatus, an electronic device, and a computer-readable storage medium. The method comprises: performing feature point detection on an image so as to obtain a feature point; acquiring a first image region manually marked on the image; adjusting the first image region according to the feature point, so as to obtain a second image region; and performing matting on the image according to the second image region. The manually marked first image region is adjusted according to the feature point, so as to acquire the second image region which is more accurately positioned, and then matting can be performed according to the second image region so as to accurately extract a required region.

An image distortion correction system includes an image data acquirer configured to receive image data from a camera, crop an image of the image data according to cropping control information generated by an image corrector, and stores the image data of the cropped image into a memory; the image corrector configured to retrieve the image data of the cropped image from the memory, correct the cropped image, and send the image data of the corrected image to a display driver for display; a display follower configured to generate a display line-field synchronization signal and send the display line-field synchronization signal to the display driver; the display driver; and the memory.

A console includes a CPU that acquires an image to be processed which is an object to be subjected to a support process that is a diagnosis support process or an imaging support process and selects a support process to be applied to the image to be processed from a plurality of processes of the support process on the basis of a part of a subject which is included in the image to be processed.

A camera device includes an imaging unit for capturing an image of an imaging area in which a subject is present, a memory for storing a camera parameter related to imaging, a detection unit for detecting the subject from the captured image, a first determination unit for primarily determining a color of a target portion of the detected subject, a second determination unit for adjusting a determination result of a predetermined color corresponding to the target portion determined by the first determination unit based on the camera parameter, when the color of the target portion determined by the first determination unit is the predetermined color having a plurality of gradations, and a communication unit for transmitting an adjustment result of the predetermined color corresponding to the determined target portion and information on the target portion to an external device in association with each other.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

Disclosed are techniques for improving an advanced driver-assistance system (ADAS) by pre-processing image data. In one embodiment, a method is disclosed comprising receiving one or more image frames captured by an image sensor installed on a vehicle; identifying a position of a skyline in the one or more image frames, the position comprising a horizontal position of the skyline; cropping one or more future image frames based on the position of the skyline, the cropping generating cropped images comprising a subset of the corresponding future image frames; and processing the cropped images at an advanced driver-assistance system (ADAS).

A method can include obtaining, from a video having a first resolution, a set of frames having a second resolution. The first resolution can be higher than the second resolution. The set of frames can include a first frame and a second frame. The method can include generating a difference feature map. The method can include obtaining a third frame having the first resolution. The method can include detecting, based on the difference feature map, a first location of a first object in the third frame. The method can include cropping, from the third frame, a first cropped area. The first cropped area can be smaller than a third frame area. The method can include generating, based on a feature map and the difference feature map, a spatial attention layer. The method can include detecting, by the spatial attention layer, the first object in the first cropped area.

Disclosed are target tracking methods and apparatuses. The target tracking apparatus performs target tracking on an input image obtained in a first time period within a single time frame, using a light neural network in a second time period of the single time frame. The target tracking apparatus may perform target tracking on input images generated within the same time frame.

An inspection system for a vehicle underbody in an in-line process includes: a vehicle recognition unit for acquiring a vehicle ID by recognizing a vehicle entering an inspection process; a vision system that photographs the vehicle underbody through a plurality of cameras disposed under a vehicle moving direction (Y-axis) and disposed at vertical and diagonal angles along a width direction (X-axis) of the vehicle; and an inspection server that detects assembly defects of a component by performing at least one of a first vision inspection that matches an object image for each component through a rule-based algorithm or a secondary deep-learning inspection through a deep-learning engine by acquiring a vehicle underbody image photographed by operating the vision system with setting information suitable for a vehicle type and a specification according to the vehicle ID.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.