Authors: V. Asha; N.U. Bhajantri; P. Nagabhushan
Addresses: Department of Computer Applications, New Horizon College of Engineering, Outer Ring Road, Marathahalli, Panathur Post, Bangalore – 560 103, Karnataka, India; JSS Research Foundation, SJCE Campus, University of Mysore, Mysore – 570 006, Karnataka, India. ' Department of Computer Science Engineering, Government Engineering College, Chamarajanagar – 571 313, Mysore District, Karnataka, India. ' Department of Studies in Computer Science, University of Mysore, Mysore – 570 006, Karnataka, India
Abstract: Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., normalised histogram intersection coefficient, Bhattacharyya coefficient, Pearson product-moment correlation coefficient, Jaccard coefficient and cosine-angle coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and all other periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward's hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick.
Keywords: hierarchical clustering; histograms; periodicity; similarity measures; automatic defect detection; patterned textures; fabric images; image recognition.
International Journal of Information and Communication Technology, 2012 Vol.4 No.2/3/4, pp.118 - 131
Available online: 29 Aug 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article