A constrained minimisation approach to optimise Gabor filters for detecting flaws in woven textiles

Gabor filters have proved to be an effective segmentation and flaw detection tool. This study addresses the issue of an optimal 2-D Gabor filter design for automatically detecting defects in homogeneously textured woven fabrics. The parameters of these filters are derived through an optimisation process performing the minimisation of a Fisher cost function. By constraining some of the Gabor filter parameters to specific values the aim is to optimise the filter to detect a certain type of flaw as it appears in a particular textile background. To account for the potentially large variety of flaw types, the optimal parameters for multiple sets of constraints are computed. The detection outcomes from each set of optimal filters are combined to produce a final classification result. Successful detection results (with low false alarm rates) suggest that this optimal Gabor filter approach is a promising method for automated detection of flaws in homogenous textiles.