Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

Yunqi Wang

Lili Chen

Minglei Chu

Hao Zhang

Shuo Zhang

Gang Li

Chenxi Zhao

Xiangjun Peng

Yachong Xue

Yaoyu Lv

Huidong He

Xiaolei Liu

Yadong Ding

The disclosure provides a method for defect classification, including: extracting a low-level feature of a defect region from a defect image; encoding the low-level feature by using a defect dictionary to obtain a middle-level semantic feature corresponding to the low-level feature; classifying a defect in the defect region of the defect image into one of a plurality of defect categories based on the middle-level semantic feature, wherein the defect dictionary includes a defect intra-category dictionary and a defect inter-category dictionary.

Method for defect classification, method for training defect classifier, and apparatus thereof

A charge release circuit includes a plurality of pixel circuits and at least one photosensitive control circuit, and each photosensitive control circuit is connected to at least one pixel circuit. Each pixel circuit of the at least one pixel circuit includes: a discharge circuit connected to the photosensitive control circuit, and a first capacitor connected to the discharge circuit and configured to store a display driving signal. The photosensitive control circuit is configured to control the discharge circuit to be turned on in a state where a backlight of a display device is turned off to release residual charges in the first capacitor.

Charge release circuit and driving method therefor, and display device

A method for detecting dirt on a screen, a device for the same, an electronic device and a computer-readable storage medium are provided. The method includes acquiring a first image of the screen when the screen is displaying a first picture, determining a dirt detection area in the first image with a mask image, and detecting a position of the dirt within the dirt detection area. With the method provided by the embodiments of the disclosure, the positon of dirt on the screen could be detected based on at most two images of the screen. The time for detecting dirt on the screen is greatly shortened, and the efficiency of detection is enhanced.

Dirt detection on screen

A method for detecting image defects is described, which includes obtaining an image to be detected, down-sampling the image to be detected to obtain a down-sampled image, de-cluttering the down-sampled image to obtain a de-cluttered image, restoring the de-cluttered image into a restored image having the same resolution as the image to be detected so as to be used as a background image, and comparing the image to be detected with the background image to determine defects in the image to be detected. An apparatus for detecting image defects, a computing device and a storage medium are also described.

Method and apparatus for detecting image defects, computing device, and computer readable storage medium

An image classification method, a computer device, and a medium are disclosed. The method includes: acquiring a middle-level semantic feature of an image to be classified through a visual dictionary; and classifying the image to be classified according to the middle-level semantic feature of the image to be classified using a classification model based on middle-level semantic features.

Image classification method, computer device and medium

An image registration method, an image registration device, and a storage medium. The image registration method includes: causing a display device to display at least one spot array; obtaining a feature image, and performing a feature-based image registration operation on the feature image to obtain at least one transformed image; and obtaining a mapping model based on the at least one transformed image. The feature image is an image which is shown on the display device and displays the at least one spot array.

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Beijing, China

Yunqi Wang