Tilings and patterns
A fractal image analysis systems for fabric inspection based on a box-counting method
Computer Networks and ISDN Systems - Special issue on graphics research and education on the World Wide Web
Dynamical Gaussian mixture model for tracking elliptical living objects
Pattern Recognition Letters
A visual approach for driver inattention detection
Pattern Recognition
Automated defect inspection and classification of leather fabric
Intelligent Data Analysis
Motif-based defect detection for patterned fabric
Pattern Recognition
Pattern Recognition Letters
An automated inspection system for textile fabrics based on Gabor filters
Robotics and Computer-Integrated Manufacturing
Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model
Pattern Recognition Letters
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
Active curve axis Gaussian mixture models
Pattern Recognition
Clustering ellipses for anomaly 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 presents a study of using ellipsoidal decision regions for motif-based patterned fabric defect detection, the result of which is found to improve the original detection success using max-min decision region of the energy-variance values. In our previous research, max-min decision region was found to be effective in distinct cases but ill detect the ambiguous false-positive and false-negative cases. To alleviate this problem, we first assume that the energy-variance values can be described by a Gaussian mixture model. Second, we apply k-means clustering to roughly identify the various clusters that make up the entire data population. Third, convex hull of each cluster is employed as a basis for fitting an ellipsoidal decision region over it. Defect detection is then based on these ellipsoidal regions. To validate the method, three wallpaper groups are evaluated using the new ellipsoidal regions, and compared with those results obtained using the max-min decision region. For the p2 group, success rate improves from 93.43% to 100%. For the pmm group, success rate improves from 95.9% to 96.72%, while the p4m group records the same success rate at 90.77%. This demonstrates the superiority of using ellipsoidal decision regions in motif-based defect detection.