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Two Compound Random Field Texture Models

  • Michal HaindlEmail author
  • Vojtěch Havlíček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)

Abstract

Two novel models for texture representation using parametric compound random field models are introduced. These models consist of a set of several sub-models each having different characteristics along with an underlying structure model which controls transitions between them. The structure model is a two-dimensional probabilistic mixture model either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional causal auto-regressive models. Both presented compound random field models allow to reproduce, compress, edit, and enlarge a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally both measured and synthetic textures are visually indiscernible.

Keywords

Texture Texture synthesis Compound random field model CAR model Two-dimensional Bernoulli mixture Two-dimensional Gaussian mixture Bidirectional texture function 

Notes

Acknowledgements

This research was supported by the Czech Science Foundation project GAČR 14-10911S.

References

  1. 1.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc., B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Figueiredo, M., Leitao, J.: Unsupervised image restoration and edge location using compound Gauss - Markov random fields and the MDL principle. IEEE Trans. Image Process. 6(8), 1089–1102 (1997)CrossRefGoogle Scholar
  3. 3.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(11), 721–741 (1984)CrossRefzbMATHGoogle Scholar
  4. 4.
    Grim, J., Haindl, M.: Texture modelling by discrete distribution mixtures. Comput. Stat. Data Anal. 41(3–4), 603–615 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Haindl, M., Havlíček, V.: A multiscale colour texture model. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.) Proceedings of the 16th International Conference on Pattern Recognition, pp. 255–258. IEEE Computer Society, Los Alamitos, August 2002. http://dx.doi.org/10.1109/ICPR.2002.1044676
  6. 6.
    Haindl, M., Havlíček, V.: A compound MRF texture model. In: Proceedings of the 20th International Conference on Pattern Recognition, ICPR 2010, pp. 1792–1795. IEEE Computer Society CPS, Los Alamitos, August 2010. http://doi.ieeecomputersociety.org/10.1109/ICPR.2010.442
  7. 7.
    Haindl, M., Remeš, V., Havlíček, V.: Potts compound Markovian texture model. In: Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012, pp. 29–32. IEEE Computer Society CPS, Los Alamitos, November 2012Google Scholar
  8. 8.
    Haindl, M.: Visual data recognition and modeling based on local Markovian models. In: Florack, L., Duits, R., Jongbloed, G., Lieshout, M.C., Davies, L. (eds.) Mathematical Methods for Signal and Image Analysis and Representation, Computational Imaging and Vision, vol. 41, chap. 14, pp. 241–259. Springer, Heidelberg (2012). http://dx.doi.org/10.1007/978-1-4471-2353-8_14
  9. 9.
    Haindl, M., Filip, J.: Visual Texture. Advances in Computer Vision and Pattern Recognition. Springer-Verlag, London (2013)CrossRefGoogle Scholar
  10. 10.
    Haindl, M., Havlíček, V.: A plausible texture enlargement and editing compound Markovian model. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds.) MUSCLE 2011. LNCS, vol. 7252, pp. 138–148. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32436-9_12. http://www.springerlink.com/content/047124j43073m202/ CrossRefGoogle Scholar
  11. 11.
    Jeng, F.C., Woods, J.W.: Compound Gauss-Markov random fields for image estimation. IEEE Trans. Sig. Process. 39(3), 683–697 (1991)CrossRefGoogle Scholar
  12. 12.
    Molina, R., Mateos, J., Katsaggelos, A., Vega, M.: Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Trans. Image Proc. 12(12), 1642–1654 (2003)CrossRefGoogle Scholar
  13. 13.
    Wu, J., Chung, A.C.S.: A segmentation model using compound Markov random fields based on a boundary model. IEEE Trans. Image Process. 16(1), 241–252 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Institute of Information Theory and Automation of the Czech Academy of SciencesPragueCzech Republic

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