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Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT)
Author(s): Bardia Yousefi; Stefano Sfarra; Clemente Ibarra Castanedo; Xavier P. V. Maldague
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Paper Abstract

Thermal and infrared imagery creates considerable developments in Non-destructive Testing (NDT) area. An analysis for thermal NDT inspection is addressed applying a new technique for computation of eigen-decomposition (factor analysis) similar to Principal Component Thermography(PCT). It is referred as Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). The proposed approach uses a computational short-cut to estimate covariance matrix and Singular Value Decomposition(SVD) to obtain faster PCT results, but while the dimension of the data increases. The problem of computational cost for high-dimensional thermal image acquisition is also investigated. Three types of specimens (CFRP, plexiglass and aluminum) have been used for comparative benchmarking. Then, a clustering algorithm segments the defect at the surface of the specimens. The results conclusively indicate the promising performance and demonstrated a confirmation for the outlined properties.

Paper Details

Date Published: 5 May 2017
PDF: 7 pages
Proc. SPIE 10214, Thermosense: Thermal Infrared Applications XXXIX, 102141I (5 May 2017); doi: 10.1117/12.2263118
Show Author Affiliations
Bardia Yousefi, Univ. Laval (Canada)
Stefano Sfarra, Univ. degli Studi dell'Aquila (Italy)
Tomsk Polytechnic Univ. (Russian Federation)
Clemente Ibarra Castanedo, Univ. Laval (Canada)
Xavier P. V. Maldague, Univ. Laval (Canada)


Published in SPIE Proceedings Vol. 10214:
Thermosense: Thermal Infrared Applications XXXIX
Paolo Bison; Douglas Burleigh, Editor(s)

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