Defect Detection in Textiles with Co-occurrence Matrix as a Texture Model Description

  • Karolina NurzynskaEmail author
  • Michał Czardybon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11255)


Automatized inspection at textile production lines becomes very important. However, there is still a need to design methods which meet not only demands concerning accuracy of defect detection, but also ones related to the processing time. In this work, a novel approach for defect model definition is presented. It is derived from the idea of co-occurrence matrix. Due to scale incorporation and binarization of the model content it proved to be a very powerful descriptor of the novelties. Moreover, it also satisfies the requirements of short processing time. The defect mask achieved with the introduced method was compared visually to other popular solutions and show a very high accuracy and quality of defect description. The processing time is real-time as the response for a 1MP (megapixel) image is reached within tens of milliseconds.


Defect detection Image segmentation Co-occurrence matrix 



This work has been based on the results of the project “Opracowanie systemu do efektywnej integracji aplikacji wizyjnych przez użyt- kowników końcowych” co-financed by the European Regional Development Fund under Operational Programme Innovative Economy 2007–2013, based on the Agreement no. UDA-POIG.01.04.00-24-067/11-00.


  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). Scholar
  2. 2.
    Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)CrossRefGoogle Scholar
  3. 3.
    Blanchard, G., Lee, G., Scott, C.: Semi-supervised novelty detection. J. Mach. Learn. Res. 11, 2973–3009 (2010)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Böttger, T., Ulrich, M.: Real-time texture error detection on textured surfaces with compressed sensing. Pattern Recogn. Image Anal. 26(1), 88–94 (2016)CrossRefGoogle Scholar
  5. 5.
    Ding, X., Li, Y., Belatreche, A., Maguire, L.P.: An experimental evaluation of novelty detection methods. Neurocomputing 135, 313–327 (2014)CrossRefGoogle Scholar
  6. 6.
    Han, Y., Shi, P.: An adaptive level-selecting wavelet transform for texture defect detection. Image Vis. Comput. 25(8), 1239–1248 (2007)CrossRefGoogle Scholar
  7. 7.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)CrossRefGoogle Scholar
  8. 8.
    Hoseini, E., Farhadi, F., Tajeripour, F.: Fabric defect detection using auto-correlation function. Int. J. Comput. Theory Eng. 5, 114–117 (2013)CrossRefGoogle Scholar
  9. 9.
    Hu, G.H.: Automated defect detection in textured surfaces using optimal elliptical gabor filters. Optik - Int. J. Light Electron Opt. 126(14), 1331–1340 (2015)CrossRefGoogle Scholar
  10. 10.
    Iyer, M., Janakiraman, S.: Defect detection in pattern texture analysis. In: 2014 International Conference on Communication and Signal Processing, pp. 172–175, April 2014Google Scholar
  11. 11.
    Latif-Amet, A., Ertüzün, A., Erçil, A.: An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image Vis. Comput. 18(6), 543–553 (2000)CrossRefGoogle Scholar
  12. 12.
    Navarro, P., Fernandez-Isla, C., Alcover, P., Suardiaz, J.: Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level. Sensors (Bassel) 16, 1178 (2016)CrossRefGoogle Scholar
  13. 13.
    Nurzynska, K., Kubo, M., Muramoto, K.: Snow particle automatic classification with texture operators. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, pp. 2892–2895, July 2011Google Scholar
  14. 14.
    Nurzynska, K., Kubo, M., Muramoto, K.: Texture operator for snow particle classification into snowflake and graupel. Atmos. Res. 118, 121–132 (2012)CrossRefGoogle Scholar
  15. 15.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000). Scholar
  16. 16.
    Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)CrossRefGoogle Scholar
  17. 17.
    Randen, T., Husoy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)CrossRefGoogle Scholar
  18. 18.
    Sari, L., Ertüzün, A.: Texture defect detection using independent vector analysis in wavelet domain. In: 2014 22nd International Conference on Pattern Recognition, pp. 1639–1644, August 2014Google Scholar
  19. 19.
    Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS 1999, pp. 582–588. MIT Press, Cambridge (1999)Google Scholar
  20. 20.
    Vaidelienė, G., Valantinas, J.: The use of Haar wavelets in detecting and localizing texture defects. Image Anal. Stereol. 35(3), 195–201 (2016)CrossRefGoogle Scholar
  21. 21.
    Xie, X., Mirmehdi, M.: TEXEMS: texture exemplars for defect detection on random textured surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1454–1464 (2007)CrossRefGoogle Scholar
  22. 22.
    Xie, X., Mirmehdi, M.: Texture exemplars for defect detection on random textures. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 404–413. Springer, Heidelberg (2005). Scholar
  23. 23.
    Yuan, X., Wu, L., Peng, Q.: An improved Otsu method using the weighted object variance for defect detection. Appl. Surface Sci. 349(Suppl. C), 472–484 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Future Processing Sp. z o.o.GliwicePoland
  2. 2.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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