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Fabric Textile Defect Detection, By Selection A Suitable Subset Of Wavelet Coefficients, Through Genetic Algorithm
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International Journal of Image Processing (IJIP)
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Volume:  5    Issue:  1
Pages:  1-108
Publication Date:   March / April 2011
ISSN (Online): 1985-2304
Pages 
25 - 35
Author(s)  
Narges Heidari - Iran
Reza Azmi - Iran
 
Published Date   
04-04-2011 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Fabric Textile Defect Detection, Genetic Algorithm, Wavelet Coefficients 
 
 
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This paper presents a novel approach for defect detection of fabric textile. For this purpose, First, all wavelet coefficients were extracted from an perfect fabric. But an optimal subset of These coefficients can delete main fabric of image and indicate defects of fabric textile. So we used Genetic Algorithm for finding a suitable subset. The evaluation function in GA was Shannon entropy. Finally, it was shown that we can gain better results for defect detection, by using two separable sets of wavelet coefficients for horizontal and vertical defects. This approach, not only increases accuracy of fabric defect detection, but also, decreases computation time. 
 
 
 
1 K. Srinivasan and P.H. Dastor and P. Radhakrishnaihan, and S. Jayaraman “FDAS: A Knowledge-based Frame Detection Work for Analysis of Defects in Woven Textile Structures”, Journal of Textile Institute, vol. 83, pp. 431-447, (1992).
2 R. Chin, “Automated Visual Inspection Techniques and Applications: A Bibliography”, Pattern Recognition, 15(4): pp. 343–357, (1982).
3 Z. Guanng and W. Jianxia, “ Fabric Defect Detection Method Based on Image Distance Difference”, Electronic Measurement and Instruments, (2007), pp. 822 -825.
4 D. Chetverikov and A. Hanbury, “Finding Defects in Texture Using Regularity and Local Orientation “ , Pattern Recognition. 35(10), pp. 2165–2180, (2002).
5 X.Z. Yang and G. Pang and N. Yung, “ Discriminative Fabric Defect Detection Using Adaptive Wavelets”, Optical Engineering, pp. 3116-3126, December (2002).
6 X.Z. Yang and G. Pang and N. Yung, “ Robust Fabric Dfect Detection and Classification Using Multiple Adaptive Wavelets”, Image and Signal Processing, IEE Proceedings, volume 152, PP. 712-723 , (2005)
7 D. Chetverikov “ Measuring the Degree of Texture Regularity”, In Proceedings of International Conference on Pattern Recognition, 1984, volume 1, PP. 80–82, (1984).
8 Yu.zhang,zhaoyang Lu,Jing Li,"Fabric Defect Detection & classification using Gabor filters & Gaussian Mixture Model",Springer-LNCS,pp:635-644,(2010)
9 HAN leil,LI zhong,"quick defect detection based on structure character of woven fabric image & wavelet transform",computer engineering & design",(2009)
10 Rong Fu,Meihong Shi,Hongli Wei,Huijuan chen,"fabric defect detection based on adaptive local binary patterns",international conference on robotics & bi omimetics,pp:1336- 1340,(2009)
11 S. Mallat,“ A Wavelet Tour of Signal Processing”, Academic Press, 2nd ed., San Diego, (1999)
12 S. Mallat,“A Theory for Multiresolution Signal Decomposition: the Wavelet Representation”, IEEE Transactions on Pattern Anal. and Mach. Intell., vol. 11, no. 7, pp. 674-693, (1989)
13 Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs”, AI Series. Springer-Verlag, New York, 3rd edition, (1996)
14 M. Mitchell, "Genetic Algorithm: An Overview", pp: 31-39, (1995).
15 L.W. Leung and B. King an, “Comparison of Image Data Fusion Techniques Using Entropy and INI” . 22nd Asian Conference on Remote Sensing, November (2001)
 
 
 
 
 
 
 
 
Narges Heidari : Colleagues
Reza Azmi : Colleagues
Boshra Pishgoo : Colleagues  
 
 
 
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