Location

La Jolla, CA

Start Date

1-1-1987 12:00 AM

Description

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].

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

6A

Chapter

Chapter 4: Image Analysis, Signal Processing and AI

Section

Image Analysis and Signal Processing

Pages

791-798

DOI

10.1007/978-1-4613-1893-4_90

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

An Improved Defect Classification Algorithm Based on Fuzzy Set Theory

La Jolla, CA

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].