The visual quality recognition of nonwovens using a novel wavelet based contourlet transform

In this paper, a novel wavelet based contourlet transform for texture extraction is presented. The visual quality recognition of nonwovens based on image processing approach can be considered as a special case of the application of computer vision and pattern recognition on industrial inspection. For concreteness, the method proposed in this paper can be divided into two stages, i.e., the feature extraction is solved by wavelet based contourlet transform, which is followed by the grade recognition with support vector machine (SVM). For the texture analysis, we propose a novel wavelet based contourlet transform, which can be considered as a simplified but more sufficient for texture analysis for nonwoven image compared with version of the one introduced by Eslami in theory view. In experiment, nonwoven images of five different visual quality grades are decomposed using wavelet based contourlet transform with ‘PKVA’ filter as the default filter of Laplacian Pyramid (LP) and Directional Filter Bank (DFB) at 3 levels firstly. Then, two energy-based features, norm-1 L1 and norm-2 L2, are calculated from the wavelet coefficients at the first level and contourlet coefficients of each high frequency subband. Finally, the SVM is designed to be a classifier to be trained and tested with the samples selected from the feature set. Experimental results indicate that when the nonwoven images are decomposed at 3 levels and 16 L2s are extracted, with 500 samples to train the SVM, the average recognition accuracy of test set is 99.2 %, which is superior to the comparative method using wavelet texture analysis.