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:
Nov. 21, 2023

Filed:

Feb. 05, 2023
Applicant:

Shanghai Jiao Tong University, Shanghai, CN;

Inventors:

Chenglin Li, Shanghai, CN;

Haoran Lv, Shanghai, CN;

Qin Yang, Shanghai, CN;

Junni Zou, Shanghai, CN;

Wenrui Dai, Shanghai, CN;

Hongkai Xiong, Shanghai, CN;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/46 (2022.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); H04N 23/698 (2023.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 10/426 (2022.01);
U.S. Cl.
CPC ...
G06V 10/462 (2022.01); G06T 5/002 (2013.01); G06T 5/20 (2013.01); G06V 10/426 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); H04N 23/698 (2023.01);
Abstract

The present disclosure provides a saliency prediction method and system for a 360-degree image based on a graph convolutional neural network. The method includes: firstly, constructing a spherical graph signal of an image of an equidistant rectangular projection format by using a geodesic icosahedron composition method; then inputting the spherical graph signal into the proposed graph convolutional neural network for feature extraction and generation of a spherical saliency graph signal; and then reconstructing the spherical saliency graph signal into a saliency map of an equidistant rectangular projection format by using a proposed spherical crown based interpolation algorithm. The present disclosure further proposes a KL divergence loss function with sparse consistency. The method can achieve excellent saliency prediction performance subjectively and objectively, and is superior to an existing method in computational complexity.


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