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

The detection and classification of faults is a major task for optical nond estructive testing in industrial quality control. Interferometric fringes, obtained by real-time optical measurement methods, contain a large amount o f image data with information about possible defect features. This mass of data must be reduced for further evaluation. One possible way is the filter ing of these images by applying the adaptive wavelet transform, which has b een proved to be a capable tool in the detection of structures with definit e spatial resolution. In this paper we show the extraction and classificati on of disturbances in interferometric fringe patterns, the application of s everal wavelet functions with different parameters for the detection of fau lts, and the combination of wavelet filters for fault classification. Examp les of fringe patterns of known and varying fault parameters are processed showing the trend of the extracted features in order to draw conclusions co ncerning the relation between the feature, the filter parameter, and the fa ult attributes. Real-time processing was achieved by importing video sequen ces in a hybrid optoelectronic system with digital image processing and an optical correlation module. The optical correlator system is based on liqui d-crystal spatial light modulators, which are addressed with image and filt er data. Results of digital simulation and optical realization are compared . (C) 2001 SPIE and IS&T.