Identifying defects under shadows from a series of thermal images by employing robust principal component analysis and local binary function

Yishuo Huang; Qian-Ying Yang; Keng-Tsang Hsu; Chih-Hung Chiang

doi:10.1117/12.2613152

18 April 2022 Identifying defects under shadows from a series of thermal images by employing robust principal component analysis and local binary function

Yishuo Huang, Qian-Ying Yang, Keng-Tsang Hsu, Chih-Hung Chiang

Author Affiliations +

Proceedings Volume 12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI; 120470Y (2022) https://doi.org/10.1117/12.2613152
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

Thermal infrared images have been widely employed to detect defections. However, it is challenging to identify the defects from thermal infrared images covered with shadows and noise. For a series of thermal infrared images, principal component analysis (PCA) is often applied to transform the given series into a lower-dimensional linear subspace. In the lower-dimensional subspace, a low-rank matrix is generated to serve as an optimal estimation from the series of thermal images. However, the information stored in those thermal infrared images will be kept mostly during the transformation. Full recovery of the lost information is not possible. A template containing the major information from the given series can be extracted by employing PCA. Furthermore, PCA has difficulty finding the template should interferences occur while the thermal images are captured. The unnecessary information collected associated with the interferences causes some unfavorable characteristics of the template extracted by PCA. Robust PCA (RPCA) is less susceptible to the abovementioned constraints. In this study, RPCA is employed to extract a template from a series of thermal infrared images. Local binary functions are built to restore the image free of noise by keeping the local boundaries. The defects can be readily identified from the regional boundaries. The proposed approach combining RPCA and local binary functions to analyze the given images in conjunction with level set functions. The processed results demonstrate that the proposed scheme are more effective than PCA in analyzing a series of thermal infrared images containing interferences.

Conference Presentation

Citation Download Citation

Yishuo Huang, Qian-Ying Yang, Keng-Tsang Hsu, and Chih-Hung Chiang "Identifying defects under shadows from a series of thermal images by employing robust principal component analysis and local binary function", Proc. SPIE 12047, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI, 120470Y (18 April 2022); https://doi.org/10.1117/12.2613152

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available