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

Sven Krueger; Guenther K.G. Wernicke; Wolfgang Osten; Daniel Kayser; Nazif Demoli; Hartmut Gruber

doi:10.1117/12.380068

21 March 2000 Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors

Sven Krueger, Guenther K.G. Wernicke, Wolfgang Osten, Daniel Kayser, Nazif Demoli, Hartmut Gruber

Author Affiliations +

Proceedings Volume 3966, Machine Vision Applications in Industrial Inspection VIII; (2000) https://doi.org/10.1117/12.380068
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

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Sven Krueger, Guenther K.G. Wernicke, Wolfgang Osten, Daniel Kayser, Nazif Demoli, and Hartmut Gruber "Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors", Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); https://doi.org/10.1117/12.380068

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