Performance Evaluation for Motif-Based Patterned Texture Defect Detection

Abstract

This paper carries an extensive evaluation on the performance of a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group is defined by a lattice, which contains a further constituent-motif. It utilizes the symmetry properties of motifs to calculate the energy of moving subtraction and its variance among motifs. Decision boundaries are determined by learning the distribution of those values among the defect-free and defective patterns in the energy-variance space. In this paper, shape transform for irregular motif has been demonstrated according to the three basic motif shapes: rectangle, triangle, and parallelogram. An error analysis for the misclassifications has also been delivered. In the database of fabrics and other patterned textures, a total of 381 defect-free lattices are used for formulation of boundaries while further 340 defect-free and 233 defective lattices are for testing. The motif-based method has a consistent result and reaches a detection success rate of 93.86%.