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Defect Detection Using Hidden Markov Random Fields
AIP Conference Proceedings
  • Aleksandar Dogandžić, Iowa State University
  • Nawanat Eua-Anant, Iowa State University
  • Benhong Zhang, Iowa State University
Document Type
Conference Proceeding
Publication Date
We derive an approximate maximum a posteriori (MAP) method for detecting NDE defect signals using hidden Markov random fields (HMRFs). In the proposed HMRF framework, a set of spatially distributed NDE measurements is assumed to form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. Here, the random field describes the defect signals to be estimated or detected. The HMRF models incorporate measurement locations into the statistical analysis, which is important in scenarios where the same defect affects measurements at multiple locations. We also discuss initialization of the proposed HMRF detector and apply to simulated eddy‐current data and experimental ultrasonic C‐scan data from an inspection of a cylindrical Ti 6‐4 billet.

The following article appeared in AIP Conference Proceedings 760 (2005): 704 and may be found at doi:10.1063/1.1916744.

Copyright 2005 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.
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American Institute of Physics
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Citation Information
Aleksandar Dogandžić, Nawanat Eua-Anant and Benhong Zhang. "Defect Detection Using Hidden Markov Random Fields" AIP Conference Proceedings Vol. 760 (2005) p. 704 - 711
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