Defect Feature Extraction in Eddy Current Pulsed Thermography

被引：0
|
作者：
Zhu P.-P. [1 ]
Cheng Y.-H. [1 ]
Bai L.-B. [1 ]
Tian L.-L. [1 ]
Huang J.-G. [1 ]
机构：
[1] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 05期
关键词：
Image fusion; Local sparsity; Non-destructive evaluation; Pulsed eddy current; Thermography;
D O I：
10.3969/j.issn.1001-0548.2019.05.013
中图分类号：
学科分类号：
摘要：
In non-destructive evaluation area, defect feature extraction and analysis based on eddy current pulsed thermography (ECPT) technique is a research focus. In this paper, a novel defect feature extraction approach is proposed to highlight the defect information in ECPT. The proposed approach includes entropy-based image selection, local (element-wise) sparse decomposition and image fusion. Comparing with other two common feature extraction algorithms, independent component analysis and robust principal component analysis, the proposed algorithm can extract more defect features and suppress background. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：741 / 746
页数：5
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