Infrared Vision: Visual Inspection Beyond the Visible Spectrum

  • Clemente Ibarra-Castanedo
  • Stefano Sfarra
  • Marc Genest
  • Xavier Maldague
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Infrared (IR) vision for the nondestructive testing (NDT) of materials has considerably grown in the last few years as a results of the continual technological progress and the development of advanced signal processing techniques. Typical applications can be divided into two groups: (1) reflectography/transmittography in the near (NIR) and short-wave (SWIR) infrared spectra, which have been recently applied in the inspection of semitransparent composite materials such as glass, aramid–phenolic, and a variety of natural fibers; and (2) infrared thermography, which involves the detection of surface and subsurface defects based on the differences in thermal signatures in the mid-wave (MWIR) and long-wave (LWIR) infrared bands. Infrared reflectography and thermography have shown interesting complementarities between them as well as with other NDT methods such as ultrasound testing, eddy current testing, and holographic interferometry. In this chapter, a review of classical and recent applications of infrared vision is provided and discussed in detail with examples.


Infrared Thermography Basalt Fiber Back Face Empirical Orthogonal Function Illumination Source 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Authors wish to thank Prof. Carlo Santulli, University of Camerino, Italy, for the loan of the composite natural fiber laminates used in this work and to the Canada Research Program (CRC): Multipolar Infrared Vision Canada Research Chair (MIVIM) for supporting part of this research.


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Copyright information

© Springer-Verlag London (outside the USA) 2015

Authors and Affiliations

  • Clemente Ibarra-Castanedo
    • 1
  • Stefano Sfarra
    • 2
  • Marc Genest
    • 3
  • Xavier Maldague
    • 1
  1. 1.Computer Vision and Systems LaboratoryUniversité LavalQuebecCanada
  2. 2.Las.E.R. Laboratory, Department of Industrial and Information Engineering and Economics (DIIIE)University of L’AquilaMonteluco di Roio—L’AquilaItaly
  3. 3.National Research Council CanadaOttawaCanada

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