A Moving Average Bidirectional Texture Function Model

  • Michal Havlíček
  • Michal Haindl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)


The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their appearance due to varying spatial, illumination, and viewing conditions. Corresponding enormous BTF measurements require a mathematical representation allowing extreme compression but simultaneously preserving its high visual fidelity. We present a novel BTF model based on a set of underlying mono-spectral two-dimensional (2D) moving average factors. A mono-spectral moving average model assumes that a stochastic mono-spectral texture is produced by convolving an uncorrelated 2D random field with a 2D filter which completely characterizes the texture. The BTF model combines several multi-spectral band limited spatial factors, subsequently factorized into a set of mono-spectral moving average representations, and range map to produce the required BTF texture space. This enables very high BTF space compression ratio, unlimited texture enlargement, and reconstruction of missing unmeasured parts of the BTF space.


BTF texture analysis texture synthesis data compression virtual reality moving average random field 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dana, K.J., Nayar, S.K., van Ginneken, B., Koenderink, J.J.: Reflectance and texture of real-world surfaces. In: CVPR, pp. 151–157. IEEE Computer Society (1997)Google Scholar
  2. 2.
    Haindl, M., Filip, J.: Visual Texture. Advances in Computer Vision and Pattern Recognition. Springer, London (January 2013)Google Scholar
  3. 3.
    Müller, G., Meseth, J., Sattler, M., Sarlette, R., Klein, R.: Acquisition, synthesis and rendering of bidirectional texture functions. In: Eurographics 2004, STAR - State of The Art Report, Eurographics Association, Eurographics Association, pp. 69–94 (2004)Google Scholar
  4. 4.
    Kawasaki, H., Seo, K.D., Ohsawa, Y., Furukawa, R.: Patch-based btf synthesis for real-time rendering. In: IEEE International Conference on Image Processing, ICIP, September 11-14, vol. 1, pp. 393–396. IEEE (2005)Google Scholar
  5. 5.
    Lefebvre, S., Hoppe, H.: Appearance-space texture synthesis. ACM Trans. Graph 25(3), 541–548 (2006); BTF samplingGoogle Scholar
  6. 6.
    Leung, C.S., Pang, W.M., Fu, C.W., Wong, T.T., Heng, P.A.: Tileable btf. IEEE Transactions on Visualization and Computer Graphics 13(5), 953–965 (2007)CrossRefGoogle Scholar
  7. 7.
    Haindl, M., Hatka, M.: BTF Roller. In: Chantler, M., Drbohlav, O. (eds.) Proceedings of the 4th International Workshop on Texture Analysis, Texture 2005, pp. 89–94. IEEE, Los Alamitos (2005)Google Scholar
  8. 8.
    Blinn, J.: Simulation of wrinkled surfaces. SIGGRAPH 1978 12(3), 286–292 (1978)CrossRefGoogle Scholar
  9. 9.
    Wang, L., Wang, X., Tong, X., Lin, S., Hu, S., Guo, B., Shum, H.: View-dependent displacement mapping. ACM Transactions on Graphics 22(3), 334–339 (2003)CrossRefGoogle Scholar
  10. 10.
    Haindl, M., Filip, J.: Extreme compression and modeling of bidirectional texture function. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1859–1865 (2007)CrossRefGoogle Scholar
  11. 11.
    Frankot, R.T., Chellappa, R.: A method for enforcing integrability in shape from shading algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(7), 439–451 (1988)zbMATHCrossRefGoogle Scholar
  12. 12.
    Favaro, P., Soatto, S.: 3-D shape estimation and image restoration: exploiting defocus and motion blur. Springer-Verlag New York Inc. (2007)Google Scholar
  13. 13.
    Woodham, R.: Photometric method for determining surface orientation from multiple images. Optical Engineering 19(1), 139–144 (1980)CrossRefGoogle Scholar
  14. 14.
    Li, X., Cadzow, J., Wilkes, D., Peters, R., Bodruzzaman II, M.: An efficient two dimensional moving average model for texture analysis and synthesis. In: Proceedings IEEE Southeastcon 1992, vol. 1, pp. 392–395. IEEE (1992)Google Scholar
  15. 15.
    Cole Jr., H.A.: On-line failure detection and damping measurement of aerospace structures by random decrement signatures. Technical Report TMX-62.041, NASA (May 1973)Google Scholar
  16. 16.
    Li, X.: An efficient two-dimensional FIR model for texture synthesis. PhD thesis, Vanderbilt University (1990)Google Scholar
  17. 17.
    Haindl, M., Filip, J.: A fast probabilistic bidirectional texture function model. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 298–305. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Haindl, M., Filip, J.: Fast BTF texture modelling. In: Chantler, M. (ed.) Texture 2003. Proceedings, pp. 47–52. IEEE Press, Edinburgh (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michal Havlíček
    • 1
  • Michal Haindl
    • 1
  1. 1.Institute of Information Theory and Automation of the ASCRPragueCzech Republic

Personalised recommendations