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. 24, 2020

Filed:

Mar. 29, 2019
Applicant:

University of Electronic Science and Technology of China, Sichuan, CN;

Inventors:

Yuhua Cheng, Chengdu, CN;

Chun Yin, Chengdu, CN;

Haonan Zhang, Chengdu, CN;

Xuegang Huang, Chengdu, CN;

Ting Xue, Chengdu, CN;

Kai Chen, Chengdu, CN;

Yi Li, Chengdu, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/136 (2017.01); G06K 9/34 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); G06K 9/342 (2013.01); G06T 7/00 (2013.01); G06T 7/136 (2017.01); G06T 2207/10016 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01);
Abstract

The present invention provides a method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities, such as the energy, temperature change rate during endothermic process, temperature change rate during endothermic process, average temperature, maximum temperature. The present invention extract these features (physical quantities) and cluster the selected TTRs into L clusters based on their feature vectors, which deeply digs the physical meanings contained in each TTR, makes the clustering more rational, and improves the accuracy of defect separation. Meanwhile, the present invention creates a multi-objective function to select a RTTR for each cluster based on multi-objective optimization. The multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same cluster, but also considers the dissimilarities between the RTTR and the TTRs in other clusters, the RTTR is more representative, which guarantees the accuracy of describing the defect outline.


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