Automated defect detection in textured materials using wavelet-domain hidden Markov models.

An approach that addresses defect detection in textured surfaces based on the wavelet-domain hidden Markov tree (HMT) model is proposed. The proposed scheme includes two successive stages, i.e., training and inspection. During the training process, an HMT for the wavelet transform (WT) of an a priori acquired defect-free template image is modeled using the expectation-maximization (EM) algorithm. With the trained HMT, a log-likelihood map (LLM) that consists of the likelihood of each coefficient can be efficiently constructed. This LLM provides a good classifier for discriminating defects from regular textures. By comparing the LLM of any defective sample under inspection with that of the template, a thresholding process can typically set the coefficients corresponding to the regular texture background to zero, while preserving those corresponding to defective regions. Therefore, in a reconstructed image obtained by the inverse two-dimensional WT of the modified coefficients, the texture patterns will be significantly eliminated, whereas the defective regions will be distinctly highlighted. The performance of the proposed method has been extensively evaluated by a variety of samples with different defect types, shapes, sizes, and texture backgrounds. Experimental results in comparison with other methods demonstrate the effectiveness of the proposed method on defect detection in textured surfaces. [ABSTRACT FROM AUTHOR]