Ellipsoidal decision regions for motif-based patterned fabric defect detection

Abstract

This paper presents a study of using ellipsoidal decision regions for motif-based patterned fabric defect detection, the result of which is found to improve the original detection success using max–min decision region of the energy-variance values. In our previous research, max–min decision region was found to be effective in distinct cases but ill detect the ambiguous false-positive and false-negative cases. To alleviate this problem, we first assume that the energy-variance values can be described by a Gaussian mixture model. Second, we apply k-means clustering to roughly identify the various clusters that make up the entire data population. Third, convex hull of each cluster is employed as a basis for fitting an ellipsoidal decision region over it. Defect detection is then based on these ellipsoidal regions. To validate the method, three wallpaper groups are evaluated using the new ellipsoidal regions, and compared with those results obtained using the max–min decision region. For the p2 group, success rate improves from 93.43% to 100%. For the pmm group, success rate improves from 95.9% to 96.72%, while the p4 m group records the same success rate at 90.77%. This demonstrates the superiority of using ellipsoidal decision regions in motif-based defect detection.