Textile flaw detection using optimal Gabor filters

This study presents a new automatic and fast approach to design optimised Gabor filters for textile flaw detection applications. The defect detection problem is solved by using a semi-supervised approach. The aim is to automatically discriminate between "known" nondefective background textures and "unknown" defective textures. The parameters of the optimal 2D Gabor filters are derived by constrained minimisation of a Fisher cost function. Such optimised Gabor filters are capable of detecting both structural and tonal defects. This adaptable approach can detect a large variety of flaw types, while at the same time, according for their changing appearance in different texture backgrounds. When applied to a large database of textile fabrics, accurate detection with a low false alarm rate was achieved.