Table of Contents
Journal of Photonics
Volume 2015, Article ID 376163, 9 pages
http://dx.doi.org/10.1155/2015/376163
Research Article

Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia

Received 12 August 2014; Accepted 8 December 2014

Academic Editor: Ivan Moreno

Copyright © 2015 Ali Rebhi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Sengottuvelan, A. Wahi, and A. Shanmugam, “Automatic fault analysis of textile fabric using imaging systems,” Research Journal of Applied Sciences, vol. 3, pp. 26–31, 2008. View at Google Scholar
  2. T. Vikrant and S. Gaurav, “Automatic fabric fault detection using morphological operations on bit plane,” International Journal of Engineering Research & Technology, vol. 2, pp. 856–861, 2013. View at Google Scholar
  3. Hong Kong Productivity Council, Textile Handbook 2000, The Hong Kong Cotton Spinners Association, 2000.
  4. H. Y. T. Ngan, G. K. H. Pang, and N. H. C. Yung, “Automated fabric defect detection—a review,” Image and Vision Computing, vol. 29, no. 7, pp. 442–458, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Ghazvini, S. A. Monadjemi, N. Movahhedinia, and K. Jamshidi, “Defect detection of tiles using 2D-wavelet transform and statistical features,” World Academy of Science, Engineering and Technology, vol. 37, pp. 901–904, 2009. View at Google Scholar · View at Scopus
  6. R. W. Conners, C. W. McMillin, and K. Lin, “Identifying and locating surface defects in wood: part of an automated lumber processing system,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 6, pp. 573–583, 1983. View at Google Scholar · View at Scopus
  7. D. Zhu, R. Conners, and P. Araman, “CT image sequence processing for wood defect recognition,” in Proceedings of the IEEE 23rd Southeastern Symposium on System Theory, pp. 75–79, Columbia, SC, USA, 1991. View at Publisher · View at Google Scholar
  8. C. Boukouvalas, “Color grading of randomly textured ceramic tiles using color histograms,” IEEE Transactions on Industrial Electronics, vol. 46, no. 1, pp. 219–226, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Wiltschi, A. Pinz, and T. Lindeberg, “Automatic assessment scheme for steel quality inspection,” Machine Vision and Applications, vol. 12, no. 3, pp. 113–128, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. C.-H. Chan and G. K. H. Pang, “Fabric defect detection by Fourier analysis,” IEEE Transactions on Industry Applications, vol. 36, no. 5, pp. 1267–1276, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. X. Yang, G. Pang, and N. Yung, “Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proceedings—Vision, Image and Signal Processing, vol. 152, pp. 715–723, 2005. View at Google Scholar
  12. D. M. Tsai and B. Hsiao, “Automatic surface inspection using wavelet reconstruction,” Pattern Recognition, vol. 34, no. 6, pp. 1285–1305, 2001. View at Publisher · View at Google Scholar
  13. Y. X. Zhi, G. K. H. Pang, and N. H. C. Yung, “Fabric defect detection using adaptive wavelet,” in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), pp. 3697–3700, May 2001. View at Scopus
  14. Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image and Vision Computing, vol. 25, no. 8, pp. 1239–1248, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Latif-Amet, A. Ertüzün, and A. Erçil, “An efficient method for texture defect detection: sub-band domain co-occurrence matrices,” Image and Vision Computing, vol. 18, no. 6, pp. 543–553, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Kumar and G. K. H. Pang, “Fabric defect segmentation using multichannel blob detectors,” Optical Engineering, vol. 39, no. 12, pp. 3176–3190, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Transactions on Industry Applications, vol. 38, no. 2, pp. 425–440, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Hamid, A. Alireza, and S. Esmaeil, “Defect detection in textiles using morphological analysis of optimal Gabor wavelet,” in Proceedings of the International Conference on Computer and Automation Engineering (ICCAE '09), pp. 26–30, 2009.
  19. A. Bodnarova, M. Bennamoun, and S. Latham, “Optimal gabor filters for textile flaw detection,” Pattern Recognition, vol. 35, no. 12, pp. 2973–2991, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Han and L. M. Zhang, “Fabric defect detection method based on Gabor filter mask,” in Proceedings of the WRI Global Congress on Intelligent Systems (GCIS '09), pp. 184–188, Xiamen, China, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Li and R. C. Staunton, “Optimum Gabor filter design and local binary patterns for texture segmentation,” Pattern Recognition Letters, vol. 29, no. 5, pp. 664–672, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Ozdemir and A. Ercil, “Markov random fields and Karhumen-Loeve transforms for defect inspection of textile products,” in Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, vol. 2, pp. 697–703, 1996.
  23. J. G. Campbell, C. Fraley, F. Murtagh, and A. E. Raftery, “Linear flaw detection in woven textiles using model-based clustering,” Pattern Recognition Letters, vol. 18, no. 14, pp. 1539–1548, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. C.-C. Huang and I.-C. Chen, “Neural-fuzzy classification for fabric defects,” Textile Research Journal, vol. 71, no. 3, pp. 220–224, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Kumar, “Neural network based detection of local textile defects,” Pattern Recognition, vol. 36, no. 7, pp. 1645–1659, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Jing, M. Li, H.-J. Zhang, and B. Zhang, “Unsupervised image segmentation using local homogeneity analysis,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '03), pp. II456–II459, IEEE, May 2003. View at Scopus
  27. Y.-S. Chiu and H.-D. Lin, “An innovative blemish detection system for curved LED lenses,” Expert Systems with Applications, vol. 40, no. 2, pp. 471–479, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Ding, C. Li, and Z. Liu, “Fabric defect detection scheme based on Gabor filter and PCA,” Advanced Materials Research, vol. 482–484, pp. 159–163, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Bissi, G. Baruffa, P. Placidi, E. Ricci, A. Scorzoni, and P. Valigi, “Automated defect detection in uniform and structured fabrics using Gabor filters and PCA,” Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 838–845, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. 2013, http://www.partnertextile.com/.
  31. M. Tabassian, R. Ghaderi, and R. Ebrahimpour, “Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence,” Expert Systems with Applications, vol. 38, no. 5, pp. 5259–5267, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Zhang, Z. Lu, and J. Li, “Fabric defect classification using radial basis function network,” Pattern Recognition Letters, vol. 31, no. 13, pp. 2033–2042, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. Z. Qiuping, W. Minyuan, L. Jie, and D. Dexiang, “Fabric defect detection via small scale over-complete basis set,” Textile Research Journal, vol. 84, no. 15, pp. 1634–1649, 2014. View at Publisher · View at Google Scholar