Analysis of image sequences for defect detection in composite materials (Articolo in rivista)

Type
Label
  • Analysis of image sequences for defect detection in composite materials (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • T. D'Orazio, M. Leo, C. Guaragnella, A. Distante (2007)
    Analysis of image sequences for defect detection in composite materials
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • T. D'Orazio, M. Leo, C. Guaragnella, A. Distante (literal)
Pagina inizio
  • 855 (literal)
Pagina fine
  • 864 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 4678 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Issia-CNR, DEE Politecnico di Bari (literal)
Titolo
  • Analysis of image sequences for defect detection in composite materials (literal)
Abstract
  • The problem of inspecting composite materials to detect internal defects is felt in many industrial contexts both for quality controls through production lines and for maintenance operations during in-service inspections. The analysis of the internal defects (not detectable by a visual inspection) is a di±cult task unless invasive techniques are applied. For this reason in the last years there has been an increasing interest for the development of low cost non-destructive inspection techniques that can be applied during normal routine tests without damaging materials but also with automatic analysis tools. In this paper we have addressed the problem of developing an automatic signal processing system that analyzes the time/space variations in a sequence of thermographic images and allows the identification of internal defects in composite materials that otherwise could not be detected. First of all a preprocessing technique was applied to the time /space signals to extract significant information, then an unsupervised classifier was used to extract uniform classes that characterize a range of internal defects. The experimental results demonstrate the ability of the method to recognize different regions containing several types defects. (literal)
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