An Improved Defect Classification Algorithm Based on Fuzzy Set Theory

Date
1987
Authors
Carkhuff, M.
Udpa, S.
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Altmetrics
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Research Projects
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Abstract

The characterization of defects in materials constitutes a major area of research emphasis. Characterization schemes often involve mapping of the signal onto an appropriate feature domain. Defects are usually classified by segmenting the feature space and identifying the segment in which the feature vector is located. As an example Udpa and Lord [1] map differential eddy current impedance plane signals on to the feature space using the Fourier Descriptor approach. Doctor and Harrington [2] use the Fisher Linear Discriminant method to identify elements of the feature vector that demonstrate a statistical correlation with the nature of the defect. Mucciardi [3] uses the Adaptive Learning Network to build the feature vector. In all these cases defect classification is typically accomplished by categorizing the mapped feature vectors using Pattern Recognition methods employing either distance or likelihood functions [4].

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