公开(公告)号：US12001746B2

公开(公告)日：2024-06-04

申请号：US17757305

申请日：2021-05-25

Applicant: BOE TECHNOLOGY GROUP CO., LTD. ,  PEKING UNIVERSITY

Inventor： Shuai Wang ,  Yadong Mu ,  Jie Feng ,  Yiming Bai ,  Xiangye Wei ,  Qiong Wu ,  Ge Ou ,  Nan Zhuang ,  Guoqiang Gong

IPC: G06F3/14 ,  G06T3/4038 ,  G06T5/00 ,  G06T7/194 ,  G06T7/70 ,  G06V10/25 ,  G06V10/764 ,  G06V40/10 ,  G09G3/00

CPC classification number: G06F3/14 ,  G06T3/4038 ,  G06T5/005 ,  G06T7/194 ,  G06T7/70 ,  G06V10/25 ,  G06V10/764 ,  G06V40/103 ,  G09G3/035 ,  G06V2201/07 ,  G09G2380/02

Abstract: An electronic apparatus and a method for displaying an image on a display device are disclosed. The electronic apparatus includes a display device; an image acquisition device configured to acquire a surrounding image of the display device; and a processor configured to: determine a background image of the display device according to the surrounding image; acquire a target range and a target object in the background image; determine a target image according to the background image, the target range and the target object; and control the display device to display the target image, wherein the target image excludes the target object.

公开(公告)号：US11887303B2

公开(公告)日：2024-01-30

申请号：US17423439

申请日：2020-08-17

Applicant: BEIJING BOE TECHNOLOGY DEVELOPMENT CO., LTD. ,  BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Yongming Shi ,  Qiong Wu ,  Ge Ou ,  Chun Wang

IPC: G16H30/00 ,  G06T7/10 ,  G06T7/11 ,  G06T7/33 ,  G06T7/00 ,  G16H30/40 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06V10/82 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096

Abstract: An image processing model generation method includes: inputting at least one training sample lesion image into an initial image processing model, the initial image processing model including a classification layer and a marking layer; calling the classification layer; calling the marking layer; obtaining a loss value of the at least one training sample lesion image in the initial image processing model; determining whether the loss value is within a preset range; if not, updating parameters of the initial image processing model, an image processing model with updated parameters being used as an initial image processing model in next training; and repeating above steps until the loss value is within the preset range.