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Error Concealment Method Selection in Texture Images Using Advanced Local Binary Patterns Classifier

  • Želmira Tóthová
  • Jaroslav Polec
  • Tatiana Orgoniková
  • Lenka Krulikovská
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)

Abstract

There are many error concealment techniques for image processing. In the paper, the focus is on restoration of image with missing blocks or macroblocks. In recent years, great attention was dedicated to textures, and specific methods were developed for their processing. Many of them use classification of textures as an integral part. It is also of an advantage to know the texture classification to select the best restoration technique. In the paper, selection based on texture classification with advanced local binary patterns and spatial distribution of dominant patterns is proposed. It is shown, that for classified textures, optimal error concealment method can be selected from predefined ones, resulting then in better restoration.

Keywords

Error concealment re-synthesis inpainting texture  extrapolation classification 

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References

  1. 1.
    Gilge, M., Engelhart, T., Mehlan, R.: Coding of Arbitrary Shaped Image Segments Based on a Gen. Orthogonal Transform. Image Communication 1, 153–180 (1989)Google Scholar
  2. 2.
    Kaup, A., Aach, T.: Coding of Segmented Images Using Shape-Independent Basis Functions. IEEE Trans. on Image Processing 7(7), 937–947 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Harrison, P.: A non-hierarchical procedure for re-synthesis of complex textures. In: WSCG 2001, Plzen, Ceska republika, pp. 190–197 (2001)Google Scholar
  4. 4.
    Wang, Y., Zhu, Q.F., Shaw, L.: Maximally smooth image recovery in transform coding. IEEE Transactions on Communications 41(10), 1544–1551 (1993)zbMATHCrossRefGoogle Scholar
  5. 5.
    Kotuliakova, K.: Hybrid ARQ Methods in Wireless Communication Channels, PhD. Thesis, SUT, Bratislava (2005)Google Scholar
  6. 6.
    Criminisi, A., Perez, P., Toyama, K.: Object Removal by Exemplar-Based Inpainting. In: Proceedings of CVPR 2003, pp. 721–728 (2003)Google Scholar
  7. 7.
    Meisinger, K., Kaup, A.: Spatial error concealment of corrupted image data using frequency selective extrapolation. In: Conf. Rec. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada, May 17-21, pp. III-209–III-212 (2004)Google Scholar
  8. 8.
    Polec, J., et al.: New Scheme for Region Approximation and Coding with Shape independent Transform. In: Proceedings IAPRS, vol. XXXIV, part 3A/B, pp. B214–B217 (2002)Google Scholar
  9. 9.
    Polec, J., Karlubikova, T.: Discrete Orthogonal Transform for Gappy Image Extrapolation. In: Proceedings of ICCVG 2004, Warsaw, Poland. Computational Imaging and Vision, vol. 32, pp. 222–227. Springer, Heidelberg (September 2004)Google Scholar
  10. 10.
    Brodatz, P.: Textures, a photographic album for artists and designers. Dover Publications Inc., New York (1966), http://www.ux.his.no/~tranden/brodatz.html Google Scholar
  11. 11.
  12. 12.
    Portilla, J., Simoncelli, E.: Representation and Synthesis of Visual Texture, http://www.cns.nyu.edu/~eero/texture/
  13. 13.
    Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 691–698 (June 2003)Google Scholar
  14. 14.
    Liao, S., Chung, A.C.S.: Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns. In: ICASSP 2007, pp. I-1221– I-1224 (2007)Google Scholar
  15. 15.
    Bautista, P.A., Lambino, M.A.: Co-occurrence matrices for wood texture classification. Electronics and Communication Department, Iligan Institute of Technology (2001)Google Scholar
  16. 16.
    Partio, M., Cramariuc, B., Gabbouj, M., Visa, A.: Rock Texture Retrieval using Gray Level Co-occurrence Matrix. In: NORSIG 2002, Trollfjord, Norway, October 4-7 (2002)Google Scholar
  17. 17.
    Huang, Y.-L., Chang, R.-F.: Texture features for DCT-coded image retrieval and classification. In: Proceedings of Acoustics, Speech, and Signal Processing, ICASSP 1999, March 15-19, vol. 6, pp. 3013–3016 (1999)Google Scholar
  18. 18.
    Oravec, M., Pavlovicova, J.: Face Recognition Methods Based on PCA and Feedforward Neural Networks. In: Proc. of the IJCNN 2004, Budapest, vol. 1, pp. 437–442 (2004)Google Scholar
  19. 19.
    Vyas, V.S.: Priti Rege: Automated Texture Analysis with Gabor filter. GVIP Journal 6(1), 35–41 (2006)Google Scholar
  20. 20.
    Ojala, T., Pietikainen, M.: Texture classification, http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OJALA1/texclas.htm
  21. 21.
    Apalovic, L.: Texture Classification. Thesis, UK Bratislava (2009)Google Scholar
  22. 22.
    Polec, J., Pohancenik, M., Ondrusova, S., Kotuliakova, K., Karlubikova, T.: Error Concealment for Classified Texture Images. In: EUROCON 2009, Saint Petersburg, Russia, pp. 1348–1353 (2009)Google Scholar
  23. 23.
    Blunsden, S.: Texture Classification using Non-Parametric Markov Random Fields. University of Edinburgh, School of Informatics (2004)Google Scholar
  24. 24.
    Arias, P., Caselles, V., Sapiro, G.: A Variational Framework for Non-local Image Inpainting. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 345–358. Springer, Heidelberg (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Želmira Tóthová
    • 1
  • Jaroslav Polec
    • 1
  • Tatiana Orgoniková
    • 1
  • Lenka Krulikovská
    • 1
  1. 1.Department of TelecommunicationsSlovak University of TechnologyBratislavaSlovakia

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