Automatic Fabric Inspection using GLCM-based Jensen-Shannon Divergence

Abstract

Jensen-Shannon divergence is one of the powerful information-theoretic measures that can capture mutual information between two probability distributions. In this paper, a machine vision algorithm is proposed for automatic inspection on dot patterned fabric using Jensen-Shannon divergence based on gray level co-occurrence matrix (GLCM). Input defective images are split into several periodic blocks and the gray levels are quantized from 0-255 to 0-63 to keep the GLCM compact and to reduce the computation time. Symmetric Jensen-Shannon divergence metrics are calculated from the GLCMs of each periodic block with respect to itself and all other periodic blocks to get a dissimilarity matrix. This dissimilarity matrix is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple and knot show the effectiveness of the proposed method for fabric inspection.