Boe Technology Group Co., Ltd.

Beijing Boe Display Technology Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Boe Technology Groupco., Ltd.

Honghong Jia

Fengshuo Hu

Rui Zheng

Jibo Zhao

Yanzhen Cui

Lijie Zhang

Guannan Chen

Jin Han

Ruibin Xue

Jingru Wang

Lijin Zhao

Dandan Feng

Xiaojun Gu

A handwriting forming method, a handwriting forming apparatus, and an electronic device are provided. The method includes: determining a group of plurality of trajectory points and obtaining trajectory information of each of the plurality of trajectory points, the trajectory information including a pressure value; establishing a plurality of trajectory units corresponding to the plurality of trajectory points in one-to-one correspondence based on the trajectory information; and connecting the plurality of trajectory units to form a handwriting for displaying. A size of each trajectory unit is determined based on a pressure sensing amount corresponding to the trajectory unit, and the pressure sensing amount is obtained based on the pressure value of a corresponding trajectory point. In the case where the pressure value is less than a first threshold, the pressure sensing amount is a first constant regardless of whether the pressure value of the corresponding trajectory point changes.

Handwriting forming method and apparatus, and electronic device

Provided is a control method including obtaining a first image; performing face recognition and gesture recognition on the first image; turning on a gesture control function when a first target face is recognized from the first image and a first target gesture is recognized from the first image; and returning to the act of obtaining the first image when the first target face is not recognized from the first image or the first target gesture is not recognized from the first image.

Control method, electronic device, and storage medium for facial and gesture recognition

A handwriting forming method, a handwriting forming apparatus, and an electronic device are provided. The method includes: determining a group of plurality of trajectory points according to a plurality of sampling points on a writing path on a working surface of a touch device, and obtaining trajectory information of each of the plurality of trajectory points, and the trajectory information including a coordinate, a flag bit and a pressure value; establishing a plurality of trajectory units in a raindrop shape corresponding to the plurality of trajectory points based on the trajectory information; determining a connection mode between trajectory units corresponding to adjacent trajectory points based on the trajectory information; and calculating connection points of the trajectory units according to the connection mode, and filling a polygon formed by the connection points of the trajectory units corresponding to the adjacent trajectory points, so as to form a handwriting for displaying.

Embodiments of the present disclosure provide a method and an apparatus for searching for an image and a related storage medium. The method includes obtaining reference keypoint data of a reference person in a reference image, and searching, based on the reference keypoint data, a set of candidate images for at least one target image containing at least one candidate person that has a pose similar to the reference person.

Method and apparatus for searching for an image and related storage medium

A handwriting processing method, a handwriting processing device and a non-transitory storage medium. The handwriting processing method includes: acquiring a handwriting point group corresponding to a stroke on a working surface of a touch device, the handwriting point group including a plurality of handwriting points arranged in sequence, and data of each handwriting point in the plurality of handwriting points including a coordinate and an action type, determining a plurality of model patterns corresponding to the plurality of handwriting plurality of model patterns being in one-to-one correspondence with the plurality of handwriting points; and sequentially connecting the plurality of model patterns, to determine a handwriting track for displaying corresponding to the handwriting point group.

Handwriting processing method, handwriting processing device and non-transitory storage medium

The present disclosure provides an OLED display substrate, a method for preparing the same, and a display device. The OLED display substrate includes an OLED device located on a base substrate and a packaging unit covering the OLED device. The packaging unit includes an inorganic material layer, an organic material layer, and a fluorine-doped diamond-like carbon layer located between the inorganic material layer and the organic material layer.

OLED display substrate and method for preparing the same, and display device

A handwriting processing method, a touch display device, a computer device, and a storage medium are provided. The handwriting processing method includes: determining, according to a sample point in a writing track that is input by a user by means of a touch display, a group of track points and a related parameter of each track point, the related parameter including a coordinate parameter and a time parameter; determining, according to the related parameter of each track point, a start position, an end position, and a type of a stroke contained in the writing track, where the type of the stroke includes one of a horizontal stroke, a vertical stroke, a left-falling stroke, a right-falling stroke or a lifting stroke; and determining, according to the type of the stroke, a shape of the start position of the stroke.

Handwriting processing method, touch display device, computer device and storage medium

A computer-implemented method of recognizing a facial expression of a subject in an input image is provided. The method includes filtering the input image to generate a plurality of filter response images; inputting the input image into a first neural network; processing the input image using the first neural network to generate a first prediction value; inputting the plurality of filter response images into a second neural network; processing the plurality of filter response images using the second neural network to generate a second prediction value; weighted averaging the first prediction value and the second prediction value to generate a weighted average prediction value; and generating an image classification result based on the weighted average prediction value.

Computer-implemented method of recognizing facial expression, apparatus for recognizing facial expression, method of pre-training apparatus for recognizing facial expression, computer-program product for recognizing facial expression

A sample extraction method targeting a video classification problem includes acquiring video data, parsing the video data to obtain a plurality of consecutive single-frame images corresponding to the video data, and extracting feature images from the plurality of consecutive single-frame images to compose samples, where the feature images are used to summarize content information of the video data, and the samples do not contain redundant image data.

Sample extraction method and device targeting video classification problem

The present disclosure provides a video identification method, a video identification device and a storage medium. The video identification device extracts an image and an optical flow from a video, classifies the image by using a first machine learning model to obtain a first classification result, classifies the optical flow by using a second machine learning model to obtain a second classification result, wherein a depth of the first machine learning model is larger than a depth of the second machine learning model, and fuses the first classification result and the second classification result to obtain the identification result of the video.

Video identification method, video identification device, and storage medium

A method for video classification includes: extracting an original image and an optical flow image corresponding to a to-be-classified video from the to-be-classified video; inputting the original image to a space-domain convolutional neural network model to obtain a space-domain classification result corresponding to the to-be-classified video; inputting the optical flow image to a time-domain convolutional neural network model to obtain a time-domain classification result corresponding to the to-be-categorized video, wherein the time-domain convolutional neural network model and the space-domain convolutional neural network model are convolutional neural network models of different network architectures; and merging the space-domain classification result and the time-domain classification result to obtain a classification result corresponding to the to-be-classified video.

Method and device for video classification

The present disclosure proposes an image adjustment method, including: determining a distance between a user and a display apparatus; determining, according to a relationship between the distance and a foreground image or a background image displayed on the display apparatus, a target image to which action information of the user is directed, wherein the target image comprises at least one of the foreground image and the background image; determining an adjustment manner corresponding to the action information of the user; and adjusting the target image according to the adjustment manner.

Growing community of inventors

Beijing, China

Honghong Jia