Article: GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures Journal: International Journal of Computational Vision and Robotics (IJCVR) 2011 Vol.2 No.4 pp.302 - 313 Abstract: Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of grey level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantising the grey levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of two major wallpaper groups (pmm and p4m groups). Inderscience Publishers - linking academia, business and industry through research

Title: GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures

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, Manasagangotri, Mysore – 570 006, Karnataka, India

Abstract: Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of grey level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantising the grey levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of two major wallpaper groups (pmm and p4m groups).

Keywords: Chi-square histogram; cluster; grey level co-occurrence matrix; defect detection; periodicity; patterned textures; machine vision; fabric images; wallpaper; defects; dissimilarity.

DOI: 10.1504/IJCVR.2011.045267

International Journal of Computational Vision and Robotics, 2011 Vol.2 No.4, pp.302 - 313

Published online: 03 Feb 2012 *

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Title: GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures

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