Abstract: Classical detection algorithms can not describe the defect characteristics in a better way, and it is difficult to classify the defects of similar classes. To solve these problems, an algorithm based on Gaussian Mixture Model (GMM) in nonsubsampled Contourlet domain for fabric defect recognition is proposed. First, the Nonsubsampled Contourlet Transform (NSCT) is used to obtain the representations in multi-directions, multi-scales and translation invariants, and an optimal sub-band is selected by the cost function. The difference of coefficients between defect and non-defect regions of the sub-band is smaller; therefore, the threshold is difficult to choose. The standard deviation method can effectively avoid this problem to get accurate results. After that the feature vectors are calculated by the statistical characteristics of the defect region. Then, the minimum misclassification function is used for joint estimation of the GMM parameters. Finally, the classification is completed by Bayesian classifier. Experiments on defects of nine categories show that the proposed algorithm can achieve better classification accuracy compared with the traditional algorithms.

Key words: computer application, defect classification, defect detection, nonsubsampled Contourlet transform (NSCT), Gaussian mixture model(GMM)