Fabric defect detection using undecimated wavelet transform

被引：3
作者：
Bi M. [1 ]
Sun Z. [1 ]
机构：
[1] Department of Control Science and Engineering, Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Hubei, Wuhan, 1037 Luoyu Road
来源：
Information Technology Journal | 2011年 / 10卷 / 09期
关键词：
Decomposition scale; Defect detection; Fabric defects; Image processing; Machine vision; Undecimated wavelet transform;
D O I：
10.3923/itj.2011.1701.1708
中图分类号：
学科分类号：
摘要：
In this study, a new fabric defect detection algorithm base on undecimated wavelet transform is proposed. The selection scheme of wavelet decomposition scales is investigated to set the decomposition scales adaptively to the fabric texture. The objective of the scheme is to enhance the energy of defective region and attenuate the energy of non-defective region. And the performance of detection results with different number of decomposition scales is also discussed. A simple and computationally effective data fusion scheme combined with amplitudes division of wavelet coefficients is used to fuse data from multiple scales together. And several features based on defective energy estimation are extracted from fused image. By examining the extracted features the proposed algorithm can provide not only the location of defects but also some detailed information about them which can be used for defect recognition and classification. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the defect detection algorithm. © 2011 Asian Network for Scientific Information.
引用
收藏
页码：1701 / 1708
页数：7
相关论文
共 19 条
  • [1] Bodnarova A., Bennamoun M., Latham S., Optimal Gabor filters for textile flaw detection, Pattern Recog., 35, pp. 2973-2991, (2002)
  • [2] Chan C.H., Pang G., Fabric defect detection by Fourier analysis, IEEE Trans. Ind. Appl., 36, pp. 1267-1276, (2000)
  • [3] Daubechies I., Orthogonal bases of compactly supported wavelets, Commnn. Pure Applied Math., 41, pp. 909-996, (1988)
  • [4] Escofet J., Navarro R., Millan M.S., Pladelloreans J., Detection of local defects in textiles webs using Gabor filters, Proceedings of the SPIE-The International Society for Optical Engineering, pp. 163-170, (1998)
  • [5] Jasper W.J., Gamier S.J., Potlapalli H., Texture characterization and defect detection using adaptive wavelets, Optical Engineering, 35, 11, pp. 3140-3149, (1996)
  • [6] Kumar Ajay, Pang Grantham, Defect detection in textured materials using Gabor filters, Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), 2, pp. 1041-1047, (2000)
  • [7] Kumar A., Pang G., Fabric defect segmentation using multichannel blob detectors, Opt. Eng., 39, pp. 3176-3176, (2000)
  • [8] Kumar A., Pang G.K.H., Defect detection in textured materials using optimized filters, IEEE Trans. Syst. Man Cybernet. B Cybernet., 32, pp. 553-570, (2002)
  • [9] Kumar A., Computer-vision-based fabric defect detection: A survey, Trans. Ind. Electron., 55, pp. 348-363, (2008)
  • [10] Loum G., Haba C.T., Lemoine J., Provent P., Texture characterisation and classification using full wavelet decomposition, J. Applied Sci., 7, pp. 1566-1573, (2007)
12