Defect Detection Using Hidden Markov Random FieldsAIP Conference Proceedings
Document TypeConference Proceeding
AbstractWe 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.
RightsCopyright 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.
Copyright OwnerAmerican Institute of Physics
Citation InformationAleksandar Dogandžić, Nawanat Eua-Anant and Benhong Zhang. "Defect Detection Using Hidden Markov Random Fields" AIP Conference Proceedings Vol. 760 (2005) p. 704 - 711
Available at: http://works.bepress.com/aleksandar_dogandzic/19/