Pattern Recognition and Image Analysis

, Volume 17, Issue 4, pp 663–674

Defect detection in textile fabric images using subband domain subspace analysis

Applied Problems


In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like independent component analysis (ICA), topographic independent component analysis (TICA), and Independent Subspace Analysis (ISA), is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in the present work, the aforementioned subspace analysis tools are applied to subband images. The feature vector of a subwindow of a test image is compared with that of a defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The increase performance that results from using wavelet transformation prior to subspace analysis has been discussed in detail. While it has been found that all subspace analysis methods lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities.


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  1. 1.
    L. A., Amet, A. Ertuzun, and A. Ercil, “An Efficient Method for Texture Defect Detection: Subband Domain Cooccurrence Matrices,” Image and Vision Computing, 18, 543–553, (2000).CrossRefGoogle Scholar
  2. 2.
    A. Atalay, Automated Defect Inspection of Textile Fabrics Using Machine Vision Techniques, M.S. Thesis (Bogazici University, Istanbul, Turkey, 1995).Google Scholar
  3. 3.
    A. Bodnarova, M. Bennamoun, and S. J. Latham, “Constrained Minimisation Approach to Optimise Gabor Filters for Detecting Flaws in Woven Textiles,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2000 (Istanbul, Turkey, May 2000), pp. 3606–3609.Google Scholar
  4. 4.
    J. F. Cardoso, “Multidimensional Independent Component Analysis,” in Proceedings of the IEEE, (1998), no. 10, pp. 2009–2025.Google Scholar
  5. 5.
    C.-H. Chan and K. H. Pang, “Fabric Defect Detection by Fourier Analysis,” IEEE Trans. Industry Applications, 36, 1267–1276 (2000).CrossRefGoogle Scholar
  6. 6.
    J. Chen and A. K. Jain, “A Structural Approach to Identify Defects in Textural Images,” in Proceedings IEEE International Conf. Systems, Man and Cybernetics (Beijing, 1988), pp. 29–32.Google Scholar
  7. 7.
    F. S. Cohen, Z. Fan, and S. Attali, “Automated Inspection of Textile Fabrics Using Textural Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, no. 8, 803–808, August, (1991).CrossRefGoogle Scholar
  8. 8.
    I. Daubechies, “Orthonormal Bases of Compactly Supported Wavelets,” Communications on Pure and Applied Mathematics, 41, 909–996, November, (1988).MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    P. Dewaele, P. Van Gool, and A. Oosterlinchk, “Texture Inspection with Self-Adaptive Convolution Filters,” in Proceedings 9th ICPR (Rome, Italy, 1988, November 14–17), pp. 56–60.Google Scholar
  10. 10.
    A. Ercil and B. Ozuylmaz, “Automated Visual Inspection of Metallic Surfaces,” in Proceedings the Third International Conference on Automation, Robotics and Computer Vision, (ICARCV’94), (Singapore, November 1994), pp. 1950–1954.Google Scholar
  11. 11.
    J. Hurri, Independent Component Analysis of Image Data, MS Thesis, (Helsinki University of Technology, Espoo, Finland, 1997).Google Scholar
  12. 12.
    A. Hyvarinen, “Survey on Independent Component Analysis,” Neural Computing Surveys, no. 2, 94–128 (1999).Google Scholar
  13. 13.
    A. Hyvarinen, and E. Oja, “Independent Component Analysis: Algorithms and Applications,” Neural Networks, 13, no. 4–5, 11–430 (2000).Google Scholar
  14. 14.
    A. Hyvarinen and E. Oja, “A Fast Fixed-Point Algorithm for Independent Component Analysis,” Neural Computation, 9, no. 7, 1483–1492, (1997).CrossRefGoogle Scholar
  15. 15.
    A. Hyvarinen, and P. O. Hoyer, “Emergence of Phase and Shift Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces,” Neural Computation, 12, no. 7, 1705–1720 (2000).CrossRefGoogle Scholar
  16. 16.
    A. Hyvarinen, P. O. Hoyer, and M. Inki, “Topographic Independent Component Analysis,” Neural Computation, 13, 1527–1558 (2001).CrossRefGoogle Scholar
  17. 17.
    J. Iivarinen, “Surface Defect Detection with Histogram-Based Texture Features,” in Proceedings SPIE 4197 (2000), pp. 140–145.Google Scholar
  18. 18.
    D. A. Karras, S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, and B. G. Mertzios, “Improved Defect Detection in Manufacturing Using Novel Multidimensional Wavelet Feature Extraction Involving Vector Quantization and PCA Techniques,” in Proceedings 8th Panhellenic Conference on Informatics (Nicosia, Cyprus, 2001, Nov. 7–10).Google Scholar
  19. 19.
    T. Kohonen, “Emergence of Invariant-Feature Detectors in Adaptive-Subspace Self-Organizing Map,” Biological Cybernetics, 75, 281–291 (1996).MATHCrossRefGoogle Scholar
  20. 20.
    G. Lambert and F. Bock, “Wavelet Methods for Texture Defect Detection,” in Proceedings of the IEEE International Conference on Image Processing (1997), vol. 3, pp. 201–204.CrossRefGoogle Scholar
  21. 21.
    S. Z. Li, X. Lv, and H. Zhang, “View Based Clustering of Object Appearances Based on Independent Subspace Analysis,” in Proceedings Eighth IEEE International Conference on Computer Vision (ICCV 2001) (Vancouver, BC, Canada, 7–14 July 2001), pp. 295–300.Google Scholar
  22. 22.
    S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE i Transactions on Pattern Analysis and Machine Intelligence, 11,no. 7, 674–693, July (1989).MATHCrossRefGoogle Scholar
  23. 23.
    R. Meylani, Texture Analysis Using Adaptive Two-Dimensional Lattice Filters, MS Thesis (Bogazici University, Istanbul, Turkey, 1997).Google Scholar
  24. 24.
    R. Meylani, A. Ertuzun, and A. Ercil, “A Comparative Study on the Adaptive Lattice Structures in the Context of Texture Defect Detection,” in Proceedings ICECS 96 (Rhodes, Greece, 1996, October 13–16), vol. 2, pp. 976–979.Google Scholar
  25. 25.
    A. R. Rao, A Taxonomy for Texture Description and Identification, Springer-Verlag, New York, 1990.MATHGoogle Scholar
  26. 26.
    T. R. Reed, and J. M. Hans Du Buf, “A Review of Recent Texture Segmentation and Feature Extraction Techniques,” CVGIP: Image Understanding, 57, 359–372 (1993).CrossRefGoogle Scholar
  27. 27.
    A. Serdaroglu, A. Ertuzun, and A. Ercil, “Defect Detection in Textile Fabric Images Using Wavelet Transforms and Independent Component Analysis,” Pattern Recognition and Image Understanding: New Technologies, PRIA-7-2004 (St. Petersburg, Russian Federation, 2004, Oct. 18–23).Google Scholar
  28. 28.
    O. G. Sezer, A. Ertuzun and A. Ercil, “Independent Component Analysis for Texture Defect Detection,” Pattern Recognition and Image Analysis, 14, no. 2, 303–307 (2004).Google Scholar
  29. 29.
    O. G. Sezer, A. Ertuzun, and A. Ercil, “Using Perceptual Relation of Regularity and Anisotropy in the Texture with Independent Component Model for Defect Detection,” Submitted to Pattern Recognition.Google Scholar
  30. 30.
    M. Tuceryan and A. Jain, “Texture Analysis,” in The Handbook of Pattern Recognition and Computer Vision, Eds. by C. H. Chen, L. F. Pau, and P. S. P. Wang, (World Scientific Publishing Co., 1993).Google Scholar
  31. 31.
    L. Van Gool, P. Dewaele, and A. Oosterlinck, “Survey-Texture Analysis Anno 1983,” Computer Vision, Graphics and Image Processing, 29, 336–357 (1985).CrossRefGoogle Scholar
  32. 32.
    L. Xu, “Least Mean Square Error Reconstruction Principle for Self-Organizing Neural Nets,” Neural Networks, no. 6, 627–648 (1993).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2007

Authors and Affiliations

  1. 1.Boğaziçi University, Electrical and Electronics Engineering DepartmentIstanbulTurkey
  2. 2.Faculty of Engineering and Natural SciencesSabanc1 UniversityIstanbulTurkey

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