Defect Detection in Textile Images Using Gabor Filters

This paper describes various techniques to detect defects in textile images. These techniques are based on multichannel Gabor features. The building blocks of our approaches are: a modified principal component analysis (PCA) technique, to select the most relevant features; one-class classification techniques (a global Gaussian model, a nearest neighbor method, and a local Gaussian model). Experimental results on synthetic and real fabric images testify for the good performance of the methods considered.

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