Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT)

Bardia Yousefi, Stefano Sfarra, Clemente Ibarra Castanedo, Xavier P.V. Maldague

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationThermosense
Subtitle of host publicationThermal Infrared Applications XXXIX
PublisherSPIE
Volume10214
ISBN (Electronic)9781510609297
DOIs
Publication statusPublished - 2017
EventThermosense: Thermal Infrared Applications XXXIX 2017 - Anaheim, United States
Duration: 10 Apr 201713 Apr 2017

Conference

ConferenceThermosense: Thermal Infrared Applications XXXIX 2017
CountryUnited States
CityAnaheim
Period10.4.1713.4.17

Keywords

  • candid covariance-free incremental principal component thermography
  • matrix decomposition analysis in infrared imagery
  • principal component thermography
  • Thermal image segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Yousefi, B., Sfarra, S., Ibarra Castanedo, C., & Maldague, X. P. V. (2017). Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). In Thermosense: Thermal Infrared Applications XXXIX (Vol. 10214). [102141I] SPIE. https://doi.org/10.1117/12.2263118