Estimation of Texels for Regular Mosaics Using Model-Based Interaction Maps

  • Georgy Gimel’farb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


Spatially homogeneous regular mosaics are image textures formed as a tiling, each tile replicating the same texel. Assuming that the tiles have no relative geometric distortions, the orientation and size of a rectangular texel can be estimated from a model-based interaction map (MBIM) derived from the Gibbs random field model of the texture. The MBIM specifies the structure of pairwise pixel interactions in a given training sample. The estimated texel allows us to quickly simulate a large-size prototype of the mosaic.


Training Sample Image Texture Pixel Neighbourhood Texture Synthesis Pixel Pair 
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.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications: New York (1966)Google Scholar
  2. 2.
    De Bonet, J. S.: Multiresolution sampling procedure for analysis and synthesis of texture images. In: Proc. ACM Conf. Computer Graphics SIGGRAPH’97 (1997) 361–368Google Scholar
  3. 3.
    Efros, A. A., Leung, T. K.: Texture synthesis by non-parametric sampling. In: Proc. IEEE Int. Conf. Computer Vision ICCV’99, Greece, Corfu, Sept. 1999, vol. 2 (1999) 1033–1038CrossRefGoogle Scholar
  4. 4.
    Gimel’farb, G. L.: Image Textures and Gibbs Random Fields. Kluwer Academic: Dordrecht (1999)Google Scholar
  5. 5.
    Gimel’farb, G.: Characteristic interaction structures in Gibbs texture modeling. In: Blanc-Talon, J., Popescu, D. C. (Eds.): Imaging and Vision Systems: Theory, Assessment and Applications. Nova Science: Huntington, N. Y. (2001) 71–90Google Scholar
  6. 6.
    Haralick, R. M., Shapiro, L. G.: Computer and Robot Vision, vol. 2. Addison-Wesley: Reading (1993)Google Scholar
  7. 7.
    Liang, L., Liu, C., Xu, Y., Guo, B., Shum, H. Y.: Real-Time Texture Synthesis by Patch-Based Sampling. MSR-TR-2001-40. Microsoft Research (2001)Google Scholar
  8. 8.
    Pickard, R., Graszyk, S., Mann, S., Wachman, J., Pickard, L., Campbell, L.: VisTex Database. MIT Media Lab.: Cambridge, Mass. (1995)Google Scholar
  9. 9.
    Paget, R., Longstaff, I. D.: Texture synthesis via a noncausal nonparametric multiscale Markov random field. IEEE Trans. on Image Processing 7 (1998) 925–931CrossRefGoogle Scholar
  10. 10.
    Portilla, J., Simoncelli, E. P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. Journal on Computer Vision 40 (2000) 49–71zbMATHCrossRefGoogle Scholar
  11. 11.
    Zalesny, A., Van Gool, L.: A compact model for viewpoint dependent texture synthesis. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A. (Eds.): 3D Structure from Images (Lecture Notes in Computer Science 2018) Springer: Berlin (2001) 124–143CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Georgy Gimel’farb
    • 1
  1. 1.CITR, Department of Computer Science, Tamaki CampusUniversity of AucklandAuckland 1New Zealand

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