Defect detection in textile fabric images using subband domain subspace analysis

In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like independent component analysis (ICA), topographic independent component analysis (TICA), and Independent Subspace Analysis (ISA), is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in the present work, the aforementioned subspace analysis tools are applied to subband images. The feature vector of a subwindow of a test image is compared with that of a defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The increase performance that results from using wavelet transformation prior to subspace analysis has been discussed in detail. While it has been found that all subspace analysis methods lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities.

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