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Discrepancy Norm as Fitness Function for Defect Detection on Regularly Textured Surfaces

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Pattern Recognition (DAGM/OAGM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7476))

Abstract

This paper addresses the problem of quality inspection of regular textured surfaces as, e.g., encountered in industrial woven fabrics. The motivation for developing a novel approach is to utilize the template matching principle for defect detection in a way that does not need any particular statistical, structural or spectral features to be calculated during the checking phase. It is shown that in this context template matching becomes both feasible and effective by exploiting the so-called discrepancy measure as fitness function, leading to a defect detection method that shows advantages in terms of easy configuration and low maintenance efforts.

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References

  1. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. Computer Vision and Image Understanding 110, 346–359 (2008)

    Article  Google Scholar 

  2. Beck, J., Chen, W.W.L.: Irregularities of distribution. Cambridge University Press, New York (2009)

    MATH  Google Scholar 

  3. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by probability distributions. Bull. Calcutta Math. 35, 99–109 (1943)

    MATH  Google Scholar 

  4. Bodnarova, A., Bennamoun, M., Latham, S.: Optimal Gabor filters for textile flaw detection. Pattern Recognition 35, 2973–2991 (2002)

    Article  MATH  Google Scholar 

  5. Bouchot, J.L., Stübl, G., Moser, B.: A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces. In: Proceedings of the SPIE 10th International Conference on Quality Control by Artificial Vision. SPIE, Saint Etienne (2011)

    Google Scholar 

  6. Broyden, C.G.: The convergence of a class of double-rank minimization algorithms 1. General considerations. IMA Journal of Applied Mathematics 6(1), 76–90 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen, C.M., Chen, C.C., Chen, C.C.: A comparison of texture features based on SVM and SOM. In: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 02, pp. 630–633. IEEE Computer Society, Washington, DC (2006)

    Chapter  Google Scholar 

  8. Cheng, H.D., Sun, Y.: A hierarchical approach to color image segmentation using homogeneity. IEEE Transactions on Image Processing 9(12), 2071–2082 (2000)

    Article  Google Scholar 

  9. Cohen, F., Fan, Z., Attali, S.: Automated inspection of textile fabrics using textural models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 803–808 (1991)

    Article  Google Scholar 

  10. Conci, A., Proença, C.B.: A fractal image analysis system for fabric inspection based on a box-counting method. Computer Networks and ISDN Systems 30, 1887–1895 (1998)

    Article  Google Scholar 

  11. Drimbarean, A., Whelan, P.F.: Experiments in colour texture analysis. Pattern Recognition Letters 22(10), 1161–1167 (2001)

    Article  MATH  Google Scholar 

  12. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  13. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3, 610–621 (1973)

    Article  Google Scholar 

  14. Karoui, I., Fablet, R., Boucher, J.M., Pieczynski, W.: Fusion of textural statistics using a similarity measure: application to texture recognition and segmentation. Pattern Analysis and Applications 11(3-4), 425–434 (2008)

    Article  MathSciNet  Google Scholar 

  15. Kuipers, L., Niederreiter, H.: Uniform distribution of sequences. Dover Publications, New York (2005)

    Google Scholar 

  16. Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statisitcs 22(1), 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kumar, A.: Computer-vision-based fabric defect detection: A survey. IEEE Transactions on Industrial Electronics 55, 348–363 (2008)

    Article  Google Scholar 

  18. Lizarraga-Morales, R.A., Sanchez-Yanez, R.E., Ayala-Ramirez, V.: Homogeneity Cues for Texel Size Estimation of Periodic and Near-Periodic Textures. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 220–229. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60, 91–110

    Google Scholar 

  20. Mirmehdi, M., Marik, R., Petrou, M., Kittler, J.: Iterative morphology for fault detection in stochastic textures. Electronic Letters 32, 443–444 (1996)

    Article  Google Scholar 

  21. Monadjemi, A.: Towards efficient texture classification and abnormality detection. Ph.D. thesis, University of Bristol, UK (2004)

    Google Scholar 

  22. Moser, B.: Similarity measure for image and volumetric data based on Hermann Weyl’s discrepancy measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(11), 2321–2329 (2011)

    Article  Google Scholar 

  23. Moser, B., Stübl, G., Bouchot, J.-L.: On a Non-monotonicity Effect of Similarity Measures. In: Pelillo, M., Hancock, E.R. (eds.) SIMBAD 2011. LNCS, vol. 7005, pp. 46–60. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  24. Murino, V., Bicego, M., Rossi, I.A.: Statistical classification of raw textile defects. In: 17th International Conference on Proceedings of the Pattern Recognition (ICPR 2004), vol. 4, pp. 311–314. IEEE Computer Society, Washington, DC (2004)

    Chapter  Google Scholar 

  25. Ng, H.F.: Automatic thresholding for defect detection. Pattern Recognition Letters 27, 1644–1649 (2007)

    Article  Google Scholar 

  26. Pietikäinen, M., Ojala, T.: Nonparametric texture analysis with simple spatial operator. Spectrum (1999)

    Google Scholar 

  27. Tolba, A.S., Khan, H.A., Mutawa, A.M., Alsaleem, S.M.: Decision fusion for visual inspection of textiles. Textile Research Journal 80 (2010)

    Google Scholar 

  28. Weyl, H.: Über die Gleichverteilung von Zahlen mod. Eins. Mathematische Annalen 77, 313–352 (1916)

    Article  MathSciNet  MATH  Google Scholar 

  29. Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. Electr. Letters on Computer Vision and Image Analysis 3, 1–22 (2008)

    Google Scholar 

  30. Xie, X., Mirmehdi, M.: TEXEMS: Texture exemplars for defect detection on random textured surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1454–1464 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Software Competence Center Hagenberg, Softwarepark 21, A-4232, Hagenberg, Austria

    Gernot Stübl, Peter Haslinger & Bernhard Moser

  2. Department of Knowledge-based Mathematical Systems, Altenbergerstr. 69, A-4040, Linz, Austria

    Jean-Luc Bouchot

Authors
  1. Gernot Stübl
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  2. Jean-Luc Bouchot
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  3. Peter Haslinger
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  4. Bernhard Moser
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Editor information

Editors and Affiliations

  1. Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Kronesgasse 5, 8010, Graz, Austria

    Axel Pinz

  2. Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria

    Thomas Pock , Horst Bischof  & Franz Leberl ,  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Stübl, G., Bouchot, JL., Haslinger, P., Moser, B. (2012). Discrepancy Norm as Fitness Function for Defect Detection on Regularly Textured Surfaces. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_43

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  • DOI: https://doi.org/10.1007/978-3-642-32717-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32716-2

  • Online ISBN: 978-3-642-32717-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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