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Optimal Gabor filters for textile flaw detection
Authors:A.    M.   S.
Affiliation:

Research Concentration in Computer Vision and Automation, Queensland University of Technology, GPO Box 2434, Brisbane, 4001 Qld, Australia

Abstract:The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results.
Keywords:Textile inspection   Flaw detection   Gabor filters   Texture analysis   Image processing   Computer vision   Optimisation   Segmentation   Automated parameter selection
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