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Proceedings Paper

Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors
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Paper Abstract

The detection and classification of faults is a major task for optical nondestructive testing in industrial quality control. Interferometric fringes, obtained by real-time optical measurement methods, contain a large amount of image data with information about possible defect features. This mass of data must be reduced for further evaluation. One possible way is the filtering of these images applying the adaptive wavelet transform, which has been proved to be a capable tool in the detection of structures with definite spatial resolution. In this paper we show the extraction and classification of disturbances in interferometric fringe patterns, the application of several wavelet functions with different parameters for the detection of faults, and the combination of wavelet filters for fault classification. Examples for fringe patterns of known and varying fault parameters are processed showing the trend of the extracted features in order to draw conclusions concerning the relation between the feature, the filter parameter, and the fault attributes. Real-time processing was achieved by importing video sequences in a hybrid opto-electronic system with digital image processing and an optical correlation module. The optical correlator system is based on liquid-crystal spatial light modulators, which are addressed with image and filter data. Results of digital simulation and optical realization are compared.

Paper Details

Date Published: 21 March 2000
PDF: 9 pages
Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); doi: 10.1117/12.380068
Show Author Affiliations
Sven Krueger, Humboldt Univ. zu Berlin (Germany)
Guenther K.G. Wernicke, Humboldt Univ. zu Berlin (Germany)
Wolfgang Osten, Bremer Institut fuer Angewandte Strahltechnik (Germany)
Daniel Kayser, Bremer Institut fuer Angewandte Strahltechnik (Germany)
Nazif Demoli, Univ. of Zagreb (Croatia)
Hartmut Gruber, Humboldt Univ. zu Berlin (Germany)


Published in SPIE Proceedings Vol. 3966:
Machine Vision Applications in Industrial Inspection VIII
Kenneth W. Tobin Jr.; John C. Stover, Editor(s)

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