An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

Автор: Md. Tarek Habib, M. Rokonuzzaman

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 9 vol.6, 2014 года.

Бесплатный доступ

Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.

Еще

Fabric Defect, Machine Vision, Defect Classification, Neural Network (NN), Counterpropagation Neural Network, Optimization Problem, Optimum Design Parameter

Короткий адрес: https://sciup.org/15010601

IDR: 15010601

Список литературы An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

  • C.-C. Wang, B. C. Jiang, J.-Y. Lin, and C.-C. Chu, “Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient,” IEEE Transactions on Semiconductor Manufacturing, vol. 26, issue 3, pp. 378-384, August 2013.
  • S. Bhuvaneswari and J. Sabarathinam, “Defect Analysis Using Artificial Neural Network,” MECS International Journal of Intelligent Systems and Applications, vol. 5, no. 5, pp. 33-38, April 2013.
  • D. M. Tsai and T. Y. Huang, “Automated Surface Inspection for Statistical Textures,” Image and Vision Computing, vol. 21, pp. 307-323, 2013.
  • M. Park, J. S. Jin, S. L. Au, S. Luo, and Y. Cui, “Automated Defect Inspection Systems by Pattern Recognition,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 2, no. 2, pp. 31-42, June 2009.
  • R. Rojas, Neural Networks: A Systematic Introduction. Germany: Springer-Verlag, 1996.
  • D. Anderson and G. McNeill, “Artificial Neural Networks Technology,” Contract Report, for Rome Laboratory, contract no. F30602-89-C-0082, August 1992.
  • R. Hecht-Nielsen, “Counterpropagation networks,” Applied Optics, vol. 26, issue 23, pp. 4979-4983, 1987.
  • A. Abouelela, H. M. Abbas, H. Eldeeb, A. A. Wahdan, and S. M. Nassar, “Automated Vision System for Localizing Structural Defects in Textile Fabrics,” Pattern Recognition Letters, vol. 26, issue 10, pp. 1435-1443, July 2005.
  • J. Martinez-Alajarin, J. D. Luis-Delgado, and L. M. Tomas-Balibrea, “Automatic system for quality-based classification of marble textures,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 35, no. 4, pp. 488-497, 2005.
  • W. Kinsner, V. Cheung, K. Cannons, J. Pear, and T. Martin, “Signal classification through multifractal analysis and complex domain neural networks,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 36, no. 2, pp. 196-203, 2006.
  • F.-J. Chang, J.-M. Liang, and Y.-C. Chen, “Flood forecasting using radial basis function neural networks,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 31, no. 4, pp. 530-535, 2001.
  • Y. Yu, C.-L. Hui, T.-M Choi, and R. Au, “Intelligent Fabric Hand Prediction System with Fuzzy Neural Network,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 40, no. 6, pp. 619-629, 2010.
  • C.-T. Lin, C.-F. Juang, and C.-P. Li, “Temperature control with a neural fuzzy inference network,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 29, no. 3, pp. 440-451, 1999.
  • M. A. Selver, O. Akay, E. Ardali, A. B. Yavuz, O. Onal, and G. Ozden, “Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 39, no. 4, pp. 426-439, 2009.
  • P. J. Sanz, R. Marin, J. S. Sanchez, “Including efficient object recognition capabilities in online robots: from a statistical to a Neural-network classifier,” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 35, no. 1, pp. 87-96, 2005.
  • A. Kumar, “Computer-Vision-Based Fabric Defect Detection: A Survey,” IEEE Transactions on Industrial Electronics, vol. 55, no. 1, pp. 348-363, January 2008.
  • M. T. Habib and M. Rokonuzzaman, “A Set of Geometric Features for Neural Network Based Textile Defect Classification,” International Scholarly Research Network Artificial Intelligence, vol. 2012, 2012.
  • M. T. Habib and M. Rokonuzzaman, “Distinguishing Feature Selection for Fabric Defect Classification Using Neural Network,” Academy Publisher Journal of Multimedia, vol. 6, no. 5, pp. 416–424, October 2011.
  • R. G. Saeidi, M. Latifi, S. S. Najar, and A. Ghazi Saeidi, “Computer Vision-Aided Fabric Inspection System for On-Circular Knitting Machine,” Textile Research Journal, vol. 75, no. 6, 492-497 (2005).
  • Y. A. Karayiannis, R. Stojanovic, P. Mitropoulos, C. Koulamas, T. Stouraitis, S. Koubias, and G. Papadopoulos, “Defect Detection and Classification on Web Textile Fabric Using Multiresolution Decomposition and Neural Networks,” Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems, Pafos, Cyprus, September 1999, pp. 765-768.
  • C.-F. J. Kuo and C.-J. Lee, “A Back-Propagation Neural Network for Recognizing Fabric Defects,” Textile Research Journal, vol. 73, no. 2, pp. 147-151, 2003.
  • P. Mitropoulos, C. Koulamas, R. Stojanovic, S. Koubias, G. Papadopoulos, and G. Karayiannis, “Real-Time Vision System for Defect Detection and Neural Classification of Web Textile Fabric,” Proceedings SPIE, vol. 3652, San Jose, California, pp. 59-69, January 1999.
  • M. A. Islam, S. Akhter, and T. E. Mursalin, “Automated Textile Defect Recognition System using Computer Vision and Artificial Neural Networks,” Proceedings World Academy of Science, Engineering and Technology, vol. 13, pp. 1-7, May 2006.
  • M. A. Islam, S. Akhter, T. E. Mursalin, and M. A. Amin, “A Suitable Neural Network to Detect Textile Defects,” Neural Information Processing, SpringerLink, vol. 4233, pp. 430-438, October 2006.
  • E. Shady, Y. Gowayed, M. Abouiiana, S. Youssef, and C. Pastore, “Detection and Classification of Defects in Knitted Fabric Structures,” Textile Research Journal, vol. 76, No. 4, pp. 295-300, 2006.
  • K. L. Mak, P. Peng, and H. Y. K. Lau, “A Real-Time Computer Vision System for Detecting Defects in Textile Fabrics,” Proceedings of the IEEE International Conference on Industrial Technology, Hong Kong, China, pp. 469-474, December 2005.
  • A. Baykut, A. Atalay, A. Erçil, and M. Güler, “Real-Time Defect Inspection of Textured Surfaces,” Real-Time Imaging, vol. 6, no. 1, pp. 17–27, February 2000.
  • A. Kumar, “Neural network based detection of local textile defects,” Pattern Recognition, vol. 36, pp. 1645-1659, 2003.
  • F. S. Cohen and Z. Fan, “Rotation and Scale Invariant Texture Classification,” Proceedings of the IEEE Conf. Robot. Autom., vol. 3, pp. 1394–1399, April 1988.
  • D. A. Karras, S. A. Karkanis, and B. G. Mertzios, “Supervised and Unsupervised Neural Network Methods applied to Textile Quality Control based on Improved Wavelet Feature Extraction Techniques,” International Journal on Computer Mathematics, vol. 67, pp. 169-181, 1998.
  • K. L. Mak, P. Peng, and K. F. C. Yiu, “Fabric Defect Detection Using Morphological Filters,” Image and Vision Computing, vol. 27, issue 10, pp. 1585-1592, September 2009.
  • J. Sun and Z. Zhou, “Fabric Defect Detection Based on Computer Vision,” Springer Artificial Intelligence and Computational Intelligence, vol. 7004, pp. 86–91, 2011.
  • R. K. R. Ananthavaram, O. S. Rao, and M. H. M. K. Prasad, “Automatic Defect Detection of Patterned Fabric by using RB Method and Independent Component Analysis,” International Journal of Computer Applications, vol. 39, no. 18, pp. 52–56, February 2012.
  • Y. Li, J. Ai, and C. Sun, “Online Fabric Defect Inspection Using Smart Visual Sensors,” Sensors, vol. 13, issue 4, pp. 4659–4673, April 2013.
  • E. Hoseini, F. Farhadi, and F. Tajeripour, “Fabric Defect Detection Using Auto-Correlation Function,” Proceedings of the 3rd International Conference on Machine Vision, 2010, pp. 557-561.
  • R. S. Sabeenian, M. E. Paramasivam, and P. M. Dinesh, “Detection and Location of Defects in Handloom Cottage Silk Fabrics using MRMRFM & MRCSF,” International Journal of Technology and Engineering System, vol. 2, no. 2, pp. 172–176, Jan–March 2011.
  • D. Phillips, Image Processing in C. 2nd ed. Kansas: R & D Publications, 2000.
  • K. Mehrotra, C. K. Mohan, and S. Ranka, Elements of Artificial Neural Netwroks. India: Penram International Publishing, 1997.
  • P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Boston: Addison-Wesley, 2006.
Еще
Статья научная