Textile Fabric Defect Detection with Wavelet and Independent Component Analysis
In this work, a new model that combines the concepts of wavelet transformation and independent component analysis (ICA) is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the texture of the textile image by preprocessing has increased the performance of the system. Based on this observation, in present work independent component analysis is aimed to be applued on the subband images. The feature vector of a subwindow of the defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The performance increase that results using wavelet transformation prior to independent component analysis has been discussed in detail.