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:
Jul. 28, 2026

Filed:

Nov. 07, 2022
Applicant:

Southeast University, Jiangsu, CN;

Inventors:

Zhe Li, Jiangsu, CN;

Liya Wang, Jiangsu, CN;

Xiao Han, Jiangsu, CN;

Futian Yuan, Jiangsu, CN;

Tongyi Zhu, Jiangsu, CN;

Ruoxuan Huang, Jiangsu, CN;

Ying Gao, Jiangsu, CN;

Yinyin Cao, Suzhou, CN;

Zheng Zhou, Jiangsu, CN;

Hengyi Zhao, Jiangsu, CN;

Jie Li, Jiangsu, CN;

Assignee:

SOUTHEAST UNIVERSITY, Jiangsu, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/372 (2021.01); A61B 5/00 (2006.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/56 (2022.01); G06V 10/762 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01);
U.S. Cl.
CPC ...
A61B 5/372 (2021.01); A61B 5/7257 (2013.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/56 (2022.01); G06V 10/762 (2022.01); G06T 2207/10024 (2013.01);
Abstract

The present disclosure provides a measurement method and system based on image electroencephalogram sensitivity data for a built environment dominant color, and relates to the field of urban quality measurement. The measurement method based on image electroencephalogram sensitivity data for a built environment dominant color includes acquiring electroencephalogram data corresponding to a built environment image sample; calculating an environment dominant color sensitivity on the basis of the electroencephalogram data; extracting a dominant color feature parameter according to the built environment image sample; constructing a built environment dominant color measurement model, and training same by taking sensitivity data and a dominant color feature as an input; and inputting an environment image to be analyzed into a trained model, so as to obtain a predicted dominant color sensitivity result. Therefore, the problems that a prediction effect of a nonlinear model integrating an image color feature and an environment quality is improved.


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