Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Automatic Detection of Texture-defects using Texture-periodicity and Jensen-Shannon Divergence

  • Asha, V. (Dept. of Computer Applications, New Horizon College of Engineering, India & JSS Research Foundation, SJCE Campus, University of Mysore) ;
  • Bhajantri, N.U. (Dept. of Computer Science and Engineering, Govt. Engineering College) ;
  • Nagabhushan, P. (Dept. of Studies in Computer Science, University of Mysore)
  • Received : 2011.09.02
  • Accepted : 2012.01.02
  • Published : 2012.06.30
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Abstract

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

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References

  1. H.S.M. Coxeter and W.O.J. Moser, Generators and Relations for Discrete Groups, 4th ed., Springer-Verlag, New York, 1980.
  2. D. Schattschneider, D., "The Plane Symmetry Groups: Their Recognition and Notation," Am. Math. Monthly, Vol.85, No.6, 1978, pp.439-450. https://doi.org/10.2307/2320063
  3. H.Y.T. Ngan and G.H.K. Pang, "Regularity Analysis for Patterned Texture Inspection," IEEE Trans. on Autom. Sci. and Eng., Vol.6, No.1, 2009, pp.131-144. https://doi.org/10.1109/TASE.2008.917140
  4. H.Y.T. Ngan, G.H.K. Pang and N.H.C. Yung, "Performance Evaluation for Motif-Based Patterned Texture Defect Detection," IEEE Trans. on Autom. Sci. and Eng., Vol.7, No.1, 2010, pp.58-72. https://doi.org/10.1109/TASE.2008.2005418
  5. H.Y.T. Ngan, G.K.H. Pang, S.P. Yung and M.K. Ng, "Wavelet based methods on patterned fabric defect detection," Pattern Recognit., Vol.38, No.4, 2005, pp.559-576. https://doi.org/10.1016/j.patcog.2004.07.009
  6. H.Y.T. Ngan and G.H.K. Pang, "Novel method for patterned fabric inspection using Bollinger bands," Opt. Eng., Vol.45, No.8, 2006, pp.087202-1-15. https://doi.org/10.1117/1.2345189
  7. F. Tajeripour, E. Kabir and A. Sheikhi, "Fabric Defect Detection Using Modified Local Binary Patterns," Proc. of the Int. Conf. on Comput. Intel. and Multimed. Appl., Sivakasi, Tamilnadu, India, December, 2007, pp.261-267.
  8. H.Y.T. Ngan, G.H.K. Pang and N.H.C. Yung, "Motif-based defect detection for patterned fabric," Pattern Recognit., Vol.41, No.6, 2008, pp.1878-1894. https://doi.org/10.1016/j.patcog.2007.11.014
  9. H.Y.T. Ngan and G.H.K. Pang, "Ellipsoidal decision regions for motif-based patterned fabric defect detection," Pattern Recognit., Vol.43, No.6, 2010, pp.2132-2144. https://doi.org/10.1016/j.patcog.2009.12.001
  10. R.T. Chin and C.A. Harlow, "Automated visual inspection: A survey," IEEE Trans. on Pattern Anal. and Mach. Intel., Vol.4, No.6, 1982, pp.557-573. https://doi.org/10.1109/TPAMI.1982.4767309
  11. B.H. Khalaj and T. Kailath, "Patterned wafer inspection by high resolution spectral estimation techniques," Mach. Vision and Appl., Vol.7, 1994, pp.178-185. https://doi.org/10.1007/BF01211662
  12. P. Xie and S.U. Guan, "A golden-template self-generating method for patterned wafer inspection," Mach. Vision and Appl., Vol.12, 2000, pp.149-156. https://doi.org/10.1007/s001380050133
  13. D.M. Endres, J.E. Schindelin, "A New Metric for Probability Distributions," IEEE Trans. on Info. Theory, Vol.49, No.7, 2003, pp.1858-1860. https://doi.org/10.1109/TIT.2003.813506
  14. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed., Academic Press, CA, 2009.
  15. V. Asha, N.U. Bhajantri, P. Nagabhushan, "Automatic Detection of Texture Defects using Texture- Periodicity and Gabor Wavelets," in: Venugopal, K.R., and Patnaik, L.M. (eds.): ICIP 2011, Communication and Computer Information Series (CCIS), Vol.157, Springer-Verlag, Berlin Heidelberg, 2011, pp.548-553.
  16. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Pearson Prentice Hall, New Delhi, 2008.
  17. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., Vol.27, No.8, 2006, pp.861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  18. C.D. Brown, H.T. Davis, "Receiver operating characteristics curves and related decision measures: A tutorial," Chemom. and Intell. Lab. Syst., Vol.80, No.1, 2006, pp.24-38. https://doi.org/10.1016/j.chemolab.2005.05.004

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