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

Fingerprint

Principal Components
Nondestructive examination
Testing
infrared imagery
decomposition
carbon fiber reinforced plastics
factor analysis
Image Acquisition
Image acquisition
Carbon fiber reinforced plastics
Polymethyl Methacrylate
Factor analysis
Benchmarking
Singular value decomposition
Factor Analysis
Covariance matrix
Aluminum
polymethyl methacrylate
Clustering algorithms
imagery

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

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

Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). / Yousefi, Bardia; Sfarra, Stefano; Ibarra Castanedo, Clemente; Maldague, Xavier P.V.

Thermosense: Thermal Infrared Applications XXXIX. Vol. 10214 SPIE, 2017. 102141I.

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

Yousefi, B, Sfarra, S, Ibarra Castanedo, C & Maldague, XPV 2017, Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). in Thermosense: Thermal Infrared Applications XXXIX. vol. 10214, 102141I, SPIE, Thermosense: Thermal Infrared Applications XXXIX 2017, Anaheim, United States, 10.4.17. https://doi.org/10.1117/12.2263118
Yousefi B, Sfarra S, Ibarra Castanedo C, Maldague XPV. Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). In Thermosense: Thermal Infrared Applications XXXIX. Vol. 10214. SPIE. 2017. 102141I https://doi.org/10.1117/12.2263118
Yousefi, Bardia ; Sfarra, Stefano ; Ibarra Castanedo, Clemente ; Maldague, Xavier P.V. / Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). Thermosense: Thermal Infrared Applications XXXIX. Vol. 10214 SPIE, 2017.
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