Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition

  • Dirk Siegmund
  • Arjan Kuijper
  • Andreas Braun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


The visual detection of defects in textiles is an important application in the textile industry. Existing systems require textiles to be spread flat so they appear as 2D surfaces, in order to detect defects. In contrast, we show classification of textiles and textile feature extraction methods, which can be used when textiles are in inhomogeneous, voluminous shape. We present a novel approach on image normalization to be used in stain-defect recognition. The acquired database consist of images of piles of textiles, taken using stereo vision. The results show that a simple classifier using normalized images outperforms other approaches using machine learning in classification accuracy.


Support Vector Machine Local Binary Pattern Color Histogram Stereo Vision Local Binary Pattern Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Hong Kong Productivity Council: Textile Handbook 2000. The Hong Kong Cotton Spinners Association (2000)Google Scholar
  2. 2.
    Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55, 348–363 (2008)CrossRefGoogle Scholar
  3. 3.
    Mishra, D.: A survey-defect detection and classification for fabric texture defects in textile industry. Int. J. Comput. Sci. Inf. Secur. 13, 48 (2015)Google Scholar
  4. 4.
    Ngan, H.Y., Pang, G.K., Yung, N.H.: Automated fabric defect detection–a review. Image Vision Comput. 29, 442–458 (2011)CrossRefGoogle Scholar
  5. 5.
    Borghese, N.A., Fomasi, M.: Automatic defect classification on a production line. Intell. Ind. Syst. 1, 373–393 (2015)CrossRefGoogle Scholar
  6. 6.
    Murino, V., Bicego, M., Rossi, I.A.: Statistical classification of raw textile defects. In: ICPR 2004, pp. 311–314 (2004)Google Scholar
  7. 7.
    Rebhi, A., Benmhammed, I., Abid, S., Fnaiech, F.: Fabric defect detection using local homogeneity analysis and neural network. J. Photonics 2015 (2015)Google Scholar
  8. 8.
    Abou-Taleb, H.A., Sallam, A.T.M.: On-line fabric defect detection and full control in a circular knitting machine. AUTEX Res. J. 8, 21–29 (2008)Google Scholar
  9. 9.
    Schicktanz, K.: Automatic fault detection possibilities on nonwoven fabrics. Melliand Textilberichte 74, 294–295 (1993)Google Scholar
  10. 10.
    Islam, M.A., Akhter, S., Mursalin, T.E., Amin, M.A.: A suitable neural network to detect textile defects. In: ICONIP 2006, pp. 430–438. Springer, Heidelberg (2006)Google Scholar
  11. 11.
    Sun, J., Zhou, Z.: Fabric defect detection based on computer vision. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011. LNCS, vol. 7004, pp. 86–91. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23896-3_11 CrossRefGoogle Scholar
  12. 12.
    Siegmund, D., Kaehm, O., Handtke, D.: Rapid classification of textile fabrics arranged in piles. In: Proceedings of the 13th International Conference on Signal Processing and Multimedia Applications, SIGMAP 2016, Lisbon, Portugal, 26–28 July 2016. SciTePress (2016)Google Scholar
  13. 13.
    Gevers, T., Stokman, H.: Classifying color edges in video into shadow-geometry, highlight, or material transitions. IEEE Trans. Multimedia 5, 237–243 (2003)CrossRefGoogle Scholar
  14. 14.
    Batchelor, B.G.: Lighting and viewing techniques. In: Automated Visual Inspection (1985)Google Scholar
  15. 15.
    Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. vol. 2, pp. 807–814. IEEE (2005)Google Scholar
  16. 16.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  17. 17.
    Gevers, T., Smeulders, A.W.: Color-based object recognition. Pattern Recogn. 32, 453–464 (1999)CrossRefGoogle Scholar
  18. 18.
    Salvador, E., Cavallaro, A., Ebrahimi, T.: Shadow identification and classification using invariant color models. In: Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), vol. 3, pp. 1545–1548. IEEE (2001)Google Scholar
  19. 19.
    Zhao, G., Pietikäinen, M.: Improving rotation invariance of the volume local binary pattern operator. In: MVA, pp. 327–330 (2007)Google Scholar
  20. 20.
    Li, Z., Liu, G., Yang, Y., You, J.: Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Trans. Image Process. 21, 2130–2140 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Bay, H., Tuytelaars, T., Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi: 10.1007/11744023_32 CrossRefGoogle Scholar
  22. 22.
    Zeng, K., Wu, N., Wang, L., Yen, K.K.: Local visual feature detection and description for non-rigid 3d objects. Adv. Image Video Process. 4, 01 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany

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