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Fabric defect detection based on textured characteristics using wavelet transform

In texture defect detection, the defects can be discriminated according to the distribution ranges of wavelet coefficients between the normal and defective parts of texture images. In traditional texture defect detection methods, the normal parts of texture images have to be trained in advance. In this paper, we propose a novel method to automatically determine the training regions based on the characteristics exhibited by normal and defective texture images. In this way, the detection error can be reduced because of the avoiding of environmental changes.