Abstract Focusing on the fabric defect detection with periodic variation pattern, a fabric defect detection method based on similarity relation is proposed. Firstly, the size of the periodic model is conformed. Secondly, grounded on the equivalence class partition method, block clustering is performed according to the cycle size (template). Then, the defect blocks are located. The similarity relation between blocks is transformed into equivalence relation and a threshold segmentation strategy is put forward. Finally, the defect detection method based on neighborhood information is added to complete the detection process. Experiments show that by the proposed method the detection accuracy is improved substantially, and the detection process is simpler and more practical.
About author:: (LIANG Jiuzhen(Corresponding author), born in 1968, Ph.D., professor. His research interests include computer vision.) (GU Chengxi, born in 1994, master student. His research interests include computer vision.) (Chang Xingzhi, born in 1979, Ph.D., associate professor. His research interests include computer vision.)
[1] MAK K L, PENG P, YIU K F C. Fabric Defect Detection Using Morphological Filters. Image and Vision Computing, 2009, 27(10):1585-1592. [2] CHAN C H, PANG G K H. Fabric Defect Detection by Fourier Analysis. IEEE Transactions on Industry Applications, 2000, 36(5): 1267-1276. [3] KUMAR A, PANG G K H. Defect Detection in Textured Materials Using Gabor Filters. IEEE Transactions on Industry Applications, 2002, 38(2): 425-440. [4] YANG X Z, PANG G, YUNG N. Discriminative Training Approaches to Fabric Defect Classification Based on Wavelet Transform. Pattern Recognition, 2004, 37(5): 889-899. [5] COHEN F S, FAN Z Q, ATTALI S. Automated Inspection of Textile Fabrics Using Textural Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 803-808. [6] ZHOU J, SEMENOVICH D, SOWMYA A, et al. Sparse Dictionary Reconstruction for Textile Defect Detection // Proc of the 11th International Conference on Machine Learning and Applications. Wa-shington, USA: IEEE, 2012, I: 21-26. [7] KANG Z Q, YUAN C H, YANG Q. The Fabric Defect Detection Technology Based on Wavelet Transform and Neural Network Convergence // Proc of the IEEE International Conference on Information and Automation. Washington, USA: IEEE, 2013: 597-601. [8] NGAN H Y T, PANG G K H, YUNG S P, et al. Wavelet Based Methods on Patterned Fabric Defect Detection. Pattern Recognition, 2005, 38(4): 559-576. [9] NGAN H Y T, PANG G K H. Novel Method for Patterned Fabric Inspection Using Bollinger Bands. Optical Engineering, 2006, 45(8): 087202-1-087202-15. [10] NGAN H Y T, PANG G K H. Regularity Analysis for Patterned Texture Inspection. IEEE Transactions on Automation Science and Engineering, 2009, 6(1): 131-144. [11] TSANG C S C, NGAN H Y T, PANG G K H. Fabric Inspection Based on the Elo Rating Method. Pattern Recognition, 2016, 51: 378-394. [12] 王 磊,李天瑞.一种基于矩阵的知识粒度计算方法.模式识别与人工智能, 2013, 26(5): 447-453. (WANG L, LI T R. A Matrix-Based Approach for Calculation of Knowledge Granulation. Pattern Recognition and Artificial Intelligence, 2013, 26(5): 447-453.)
[13] 石瑞平,张素芬.等价关系的判定及性质.数学的实践与认识, 2011, 41(14): 230-233. (SHI R P, ZHANG S F. Diagnosis and Nature of the Equivalence Relation. Mathematics in Practice and Theory, 2011, 41(14): 230-233.) [14] 安秋生,沈钧毅,王国胤.基于信息粒度与Rough集的聚类方法研究.模式识别与人工智能, 2003, 16(4): 412-417. (AN Q S, SHEN J Y, WANG G Y. A Clustering Method Based on Information Granularity and Rough Sets. Pattern Recognition and Artificial Intelligence, 2003, 16(4): 412-417.) [15] NG M K, NGAN H Y T, YUAN X M, et al. Patterned Fabric Inspection and Visualization by the Method of Image Decomposition. IEEE Transactions on Automation Science and Engineering, 2014, 11(3): 943-947.
2017, Vol. 30 
