This application provides a video information processing method performed by an electronic device. The method includes: determining a video image frame set corresponding to each of a first video and a second video, respectively; determining a static stitching region corresponding to image frames in the video image frame set; cropping the image frames in the video image frame set according to the static stitching region, and determining an image feature vector for the video based on a corresponding cropping result using a video information processing model; and determining a similarity between the first video and the second video based on an image feature vector corresponding to the first video and an image feature vector corresponding to the second video.

This application provides a method and an apparatus for interaction processing of a virtual item, an electronic device, and a computer-readable storage medium. The method includes displaying at least one idle virtual item in a virtual scene; moving a first virtual object in the virtual scene in response to a movement operation on the first virtual object; displaying a pickable prompt of the idle virtual item when there is no obstacle between the idle virtual item and the first virtual object; and controlling the first virtual object to pick up the idle virtual item in response to a picking-up operation by the first virtual object.

An information processing device (200A, 200B, and 200C) according to the present disclosure includes a control unit (220, 220B, and 220C). The control unit (220, 220B, and 220C) acquires a captured image of a target imaged by a sensor. The captured image is an image obtained from reflected light of light emitted to the target from a plurality of light sources arranged at different positions, respectively. The control unit (220, 220B, and 220C) extracts a flat region from the captured image based on a luminance value of the captured image. The control unit (220, 220B, and 220C) calculates shape information regarding a shape of a surface of the target based on information regarding the sensor and the flat region of the captured image.

A face authentication apparatus (50) includes an image generation unit (102) that generates a first image by capturing an image of a person, a control unit (104) that, when the first image does not satisfy a criterion for face collation, controls lighting and causes the image generation unit (102) to generate a second image by capturing an image of the person again, and a face authentication unit (52) that executes face authentication by using the second image.

A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

A system and method of detecting an image of a template object in a captured image may include comparing, by a processor, an image model of an imaged template object to multiple locations, rotations, and scales in the captured image. The image model may be defined by multiple model base point sets derived from contours of the imaged template object, where each model base point set inclusive of a plurality of model base points that are positioned at corresponding locations associated with distinctive features of the imaged template object. Each corresponding model base point of the model base point sets may (i) be associated with respective layers and (ii) have an associated gradient vector. A determination may be made as to whether and where the image of the object described by the image model is located in the captured image.

A method of processing an image, an electronic device, and a storage medium, which relate to the artificial intelligence field, in particular to fields of computer vision and intelligent transportation technologies. The method includes: determining at least one key frame image in a scene image sequence captured by a target camera; determining a camera pose parameter associated with each key frame image in the at least one key frame image, according to a geographic feature associated with the key frame image; and projecting each scene image in the scene image sequence to obtain a target projection image according to the camera pose parameter associated with the key frame image, so as to generate a scene map based on the target projection image. The geographic feature associated with any key frame image indicates localization information of the target camera at a time instant of capturing the corresponding key frame image.

A device and method of obtaining a depth of a space are provided. The method includes obtaining a plurality of images by photographing a periphery of a camera a plurality of times while sequentially rotating the camera by a preset angle, identifying a first feature region in a first image and an n-th feature region in an n-th image, the n-th feature region being identical with the first feature region, by comparing adjacent images between the first image and the n-th image from among the plurality of images, obtaining a base line value with respect to the first image and the n-th image, obtaining a disparity value between the first feature region and the n-th feature region, and determining a depth of the first feature region or the n-th feature region based on at least the base line value and the disparity value.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.