The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 21, 2023

Filed:

Apr. 27, 2021
Applicant:

Shenzhen University, Guangdong, CN;

Inventors:

Yingying Zhu, Guangdong, CN;

Shihai Chen, Guangdong, CN;

Assignee:

Shenzhen University, Shenzhen, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04N 13/00 (2018.01); G06V 10/32 (2022.01); G06V 10/40 (2022.01); G06T 15/00 (2011.01);
U.S. Cl.
CPC ...
H04N 13/00 (2013.01); G06T 15/00 (2013.01); G06V 10/32 (2022.01); G06V 10/40 (2022.01); H04N 2013/0088 (2013.01); H04N 2013/0092 (2013.01);
Abstract

Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.


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