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Fabric Textile Defect Detection, By Selection A Suitable Subset Of Wavelet Coefficients, Through Genetic Algorithm
Narges Heidari, Reza Azmi, Boshra Pishgoo
Pages - 25 - 35     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
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KEYWORDS
Fabric Textile Defect Detection, Genetic Algorithm, Wavelet Coefficients
ABSTRACT
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.
CITED BY (19)  
1 Roy, A., Schaffer, J. D., & Laramee, C. B. (2016). A novel approach to signal classification with an application to identifying the alcoholic brain. Applied Soft Computing, 43, 406-414.
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4 Karlekar, V. V., & Biradar, M. S. (2015, September). Genetic algorithm based wavelet filter for automatic fabric defect detection. In Computer, Communication and Control (IC4), 2015 International Conference on (pp. 1-6). IEEE.
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6 Hu, G. H. (2015). Automated defect detection in textured surfaces using optimal elliptical Gabor filters. Optik-International Journal for Light and Electron Optics, 126(14), 1331-1340.
7 Tov, O. B. S., Schaffer, J. D., & McLeod, K. J. (2015). Developing an Evolutionary Algorithm to Search for an Optimal Multi-Mother Wavelet Packets Combination. Journal of Biomedical Science and Engineering, 8(7), 458.
8 Pan, R., Zhu, B., Li, Z., Liu, J., & Gao, W. (2015). A simulation method of plain fabric texture for image analysis/Metoda de simulare a structurii tesaturilor plane pentru analiza imaginii. Industria Textila, 66(1), 28.
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11 Sari, L., & Ertuzun, A. (2014, August). Texture Defect Detection Using Independent Vector Analysis in Wavelet Domain. In 2014 22nd International Conference on Pattern Recognition (ICPR) (pp. 1639-1644). IEEE.
12 Raafat, H. M., & Tolba, A. S. (2014). Homoscedasticity for defect detection in homogeneous flat surface products. Textile Research Journal, 0040517514555795.
13 Hu, G. H., Zhang, G. H., & Wang, Q. H. (2014). Automated defect detection in textured materials using wavelet-domain hidden Markov models. Optical Engineering, 53(9), 093107-093107.
14 Miyamoto Nine, incense, & Tao. (2014). Based on machine weft detector method Radon transform electronic measurement technology, (2), 58-63.
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Mr. Narges Heidari
Islamic Azad University - Iran
narges_hi@yahoo.com
Dr. Reza Azmi
- Iran
Mr. Boshra Pishgoo
- Iran


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