Tilings and patterns
Automated inspection of textured ceramic tiles
Computers in Industry
Symmetry in class and type hierarchy
CRPIT '02 Proceedings of the Fortieth International Conference on Tools Pacific: Objects for internet, mobile and embedded applications
Real-time vision-based system for textile fabric inspection
Real-Time Imaging
Computer and Robot Vision
Symmetry as a Continuous Feature
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing techniques for automatic textile quality control
Systems Analysis Modelling Simulation - Special issue: Digital signal processing and control
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated defect inspection and classification of leather fabric
Intelligent Data Analysis
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
Automated vision system for localizing structural defects in textile fabrics
Pattern Recognition Letters
Defect detection in textured materials using optimized filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automated Visual Inspection: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ellipsoidal decision regions for motif-based patterned fabric defect detection
Pattern Recognition
Review article: Automated fabric defect detection-A review
Image and Vision Computing
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures
International Journal of Computational Vision and Robotics
Similarity measures for automatic defect detection on patterned textures
International Journal of Information and Communication Technology
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This paper proposes a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2D patterned texture. It assumes that most patterned texture can be decomposed into lattices and their constituents-motifs. It then utilizes the symmetry property of motifs to calculate the energy of moving subtraction and its variance among different motifs. By learning the distribution of these values over a number of defect-free patterns, boundary conditions for discerning defective and defect-free patterns can be determined. This paper presents the theoretical foundation of the method, and defines the relations between motifs and lattice, from which a new concept called energy of moving subtraction is derived using norm metric measurement between a collection of circular shift matrices of motif and itself. It has been shown in this paper that the energy of moving subtraction amplifies the defect information of the defective motif. Together with its variance, an energy-variance space is further defined where decision boundaries are drawn for classifying defective and defect-free motifs. As the 16 wallpaper groups of patterned fabric can be transformed into three major groups, the proposed method is evaluated over these three major groups, from which 160 defect-free lattices samples are used for defining the decision boundaries, with 140 defect-free and 113 defective samples used for testing. An overall detection success rate of 93.32% is achieved for the proposed method. No other generalized approach can achieve this success rate has been reported before, and hence this result outperforms all other previously published approaches.