Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method

This paper proposed an approach, which is based on multi-scale wavelet transform and Gaussian mixture model, to solve the problem about automated fabric defect detection and improve the quality of fabric in the production. Firstly, the sample image was tackled by the "Pyramid" wavelet decomposition algorithm, and the new images were obtained by reconstructing with the produced wavelet coefficients using wavelet thresholding denoising method. Secondly, the obtained new images were segmented by applying the Gaussian mixture model that was based on the Expectation-Maximization (EM) algorithm. Various fabric samples were used in the evaluation, and the experimental results showed that the designed algorithm could precisely locate the position of defect and segment the defect.