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Dynamic Textures

  • Chapter
Visual Texture

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The ever-growing number of virtual reality applications and computer games require realistic rendering of sea waves, flowing water, smoke and many others genuine dynamic materials. Using real videos is impractical or even impossible due to memory, length or other constraints. Because the appearance of real materials or phenomena dramatically changes with variations in illumination and viewing conditions, it is often more practical to use a generative mathematical model representation of dynamic textures rather than to store several versions of a dynamic texture for required combinations of camera and light positions. Other modeling or even analytical dynamic texture applications, such as video restoration, compression or segmentation, can significantly profit from these models as well. This chapter surveys different approaches to dynamic texture modeling, which have been published up to now in this emerging computer vision & graphics area.

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References

  1. Amiaz, T., Fazekas, S., Chetverikov, D., Kiryati, N.: Detecting regions of dynamic texture. In: Scale Space and Variational Methods in Computer Vision, pp. 848–859 (2007)

    Chapter  Google Scholar 

  2. Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., Werman, M.: Texture mixing and texture movie synthesis using statistical learning. IEEE Trans. Vis. Comput. Graph. 7(2), 120–135 (2001)

    Article  Google Scholar 

  3. Bertails, F., Audoly, B., Cani, M.P., Querleux, B., Leroy, F., Lévêque, J.L.: Super-helices for predicting the dynamics of natural hair. In: SIGGRAPH’06, ACM SIGGRAPH 2006 Papers, pp. 1180–1187. ACM, New York (2006)

    Chapter  Google Scholar 

  4. Chan, A.B., Vasconcelos, N.: Mixtures of dynamic textures. In: ICCV, pp. 641–647. IEEE Comput. Soc., Los Alamitos (2005)

    Google Scholar 

  5. Chan, A.B., Vasconcelos, N.: Probabilistic kernels for the classification of auto-regressive visual processes. In: IEEE Computer Vision and Pattern Recognition or CVPR, pp. 846–851 (2005)

    Google Scholar 

  6. Chan, A.B., Vasconcelos, N.: Classifying video with kernel dynamic textures. In: IEEE Computer Vision and Pattern Recognition or CVPR, pp. 1–6 (2007)

    Google Scholar 

  7. Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 909–926 (2008)

    Article  Google Scholar 

  8. Chetverikov, D., Peteri, R.: A brief survey of dynamic texture description and recognition. In: CORES 05, Advances in Soft Computing, pp. 17–26. Springer, Berlin (2005)

    Google Scholar 

  9. Costantini, R., Sbaiz, L., Susstrunk, S.: Dynamic texture analysis and synthesis using tensor decomposition. In: Advances in Visual Computing, pp. 245–254 (2006). http://dx.doi.org/10.1007/11919629_26

    Chapter  Google Scholar 

  10. Costantini, R., Sbaiz, L., Susstrunk, S.: Dynamic texture synthesis: compact models based on luminance–chrominance color representation. In: International Conference on Image Processing, pp. 2085–2088 (2006)

    Google Scholar 

  11. Criminisi, A., Zisserman, A.: Shape from texture: homogeneity revisited. In: BMVC (2000)

    Google Scholar 

  12. Crivelli, T., Cernuschi Frias, B., Bouthemy, P., Yao, J.: Temporal modeling of motion textures with mixed-sates Markov chains. In: ICASSP, pp. 881–884. IEEE Press, New York (2008)

    Google Scholar 

  13. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Ding, T., Sznaier, M., Camps, O.I.: A rank minimization approach to video inpainting. In: International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  15. Doretto, G., Soatto, S.: Dynamic texture recognition. Tech. rep. University of California Press, Berkeley (2001)

    Google Scholar 

  16. Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. Int. J. Comput. Vis. 51(2), 91–109 (2003)

    Article  MATH  Google Scholar 

  17. Doretto, G., Cremers, D., Favaro, P., Soatto, S.: Dynamic texture segmentation. In: Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, pp. 1236–1242. IEEE, Nice, France (2003)

    Chapter  Google Scholar 

  18. Doretto, G., Jones, E., Soatto, S.: Spatially homogeneous dynamic textures. In: European Conference on Computer Vision, vol. II, pp. 591–602 (2004)

    Google Scholar 

  19. Ebert, D., Parent, R.: Rendering and animation of gaseous phenomena by combining fast volume and scanline a-buffer techniques. Comput. Graph. 24(4), 357–366 (1990)

    Article  Google Scholar 

  20. Filip, J., Haindl, M., Chetverikov, D.: Fast synthesis of dynamic colour textures. In: Tang, Y., Wang, S., Yeung, D., Yan, H., Lorette, G. (eds.) Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. IV, pp. 25–28. IEEE Comput. Soc., Los Alamitos (2006)

    Google Scholar 

  21. Fournier, A., Reeves, W.: A simple model for ocean waves. Comput. Graph. 20(4), 75–84 (1986)

    Article  Google Scholar 

  22. Ghahramani, Z., Hinton, G.E.: The EM algorithm for mixtures of factor analyzers. Tech. Rep. CRG-TR-96-1, Dpt. of Comp. Sci., Univ. of Toronto (1996)

    Google Scholar 

  23. Ghahramani, Z., Hinton, G.E.: Variational learning for switching state-space models. Neural Comput. 12(4), 831–864 (2000)

    Article  Google Scholar 

  24. Golub, G., van Loan, C.: Matrix Computations. John Hopkins University Press, Baltimore (1989)

    MATH  Google Scholar 

  25. Haindl, M., Filip, J.: Extreme compression and modeling of bidirectional texture function. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1859–1865 (2007). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1139

    Article  Google Scholar 

  26. Huang, J.Z., Huang, X.L., Metaxas, D.: Optimization and learning for registration of moving dynamic textures. In: International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  27. Iglesias, A.: Computer graphics for water modeling and rendering: a survey. Future Gener. Comput. Syst. 20(8), 1355–1374 (2004). Computer Graphics and Geometric Modeling

    Article  Google Scholar 

  28. Jin, X., Chen, S., Mao, X.: Computer-generated marbling textures: a GPU-based design system. IEEE Comput. Graph. Appl. 27(2), 78–84 (2007)

    Article  Google Scholar 

  29. Julesz, B.: Visual pattern discrimination. IRE Trans. Inf. Theory 8(1), 84–92 (1962)

    Article  Google Scholar 

  30. Julesz, B.: Textons, the elements of texture perception and their interactions. Nature 290, 91–97 (1981)

    Article  Google Scholar 

  31. Julesz, B., Gilbert, E., Victor, J.: Visual discrimination of textures with identical third-order statistics. Biol. Cybern. 31, 137–140 (1978)

    Article  Google Scholar 

  32. Kwatra, V., Schodl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. 22(3), 277–286 (2003)

    Article  Google Scholar 

  33. Li, Y., Wang, T., Shum, H.Y.: Motion texture: a two-level statistical model for character motion synthesis. ACM Trans. Graph. 21(3), 465–472 (2002)

    Article  Google Scholar 

  34. Li, R., Tian, T.P., Sclaroff, S.: Simultaneous learning of nonlinear manifold and dynamical models for high-dimensional time series. In: International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  35. Lin, R.S., Liu, C.B., Yang, M.H., Ahuja, N., Levinson, S.: Learning nonlinear manifolds from time series. In: European Conference on Computer Vision, pp. 245–256 (2006)

    Google Scholar 

  36. Liu, C.B., Sung Lin, R., Ahuja, N.: Modeling dynamic textures using subspace mixtures. In: IEEE Int. Conf. Multimedia and Expo, pp. 1378–1381. IEEE Press, New York (2005)

    Google Scholar 

  37. Liu, C.B., Lin, R.S., Ahuja, N., Yang, M.H.: Dynamic textures synthesis as nonlinear manifold learning and traversing. In: British Machine Vision Conference, p. II:859 (2006)

    Google Scholar 

  38. Luettgen, M., Karl, W., Willsky, A., Tenney, R.: Multiscale representations of Markov random fields. IEEE Trans. Signal Process. 41(12), 3377–3396 (1993)

    Article  MATH  Google Scholar 

  39. Masiero, A., Chiuso, A.: Non linear temporal textures synthesis: a Monte Carlo approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV (2). Lecture Notes in Computer Science, vol. 3952, pp. 283–294. Springer, Berlin (2006)

    Google Scholar 

  40. Nicodemus, F., Richmond J.C., Hsia, J., Ginsburg, I., Limperis, T.: Geometrical Considerations and Nomenclature for Reflectance. NBS Monograph, vol. 160, pp. 1–52. National Bureau of Standards, U.S. Department of Commerce, Washington (1977)

    Google Scholar 

  41. Peachey, D.: Modelling waves and surf. Comput. Graph. 20(4), 65–74 (1986)

    Article  Google Scholar 

  42. Perlin, K.: An image synthesizer. Comput. Graph. 19(3), 287–296 (1985)

    Article  Google Scholar 

  43. Peteri, R., Fazekas, S., Huiskes, M.J.: DynTex: a comprehensive database of dynamic textures. Pattern Recognit. Lett. 31(12), 1627–1632 (2010)

    Article  Google Scholar 

  44. Phillips, P.M., Watson, G.: Generalising video textures. In: TPCG, pp. 8–15. IEEE Comput. Soc., Los Alamitos (2003)

    Google Scholar 

  45. Polana, R., Nelson, R.: Low level recognition of human motion. In: IEEE Workshop on Motion of Non-rigid and Articulated Objects, Austin, TX, pp. 77–82 (1994)

    Google Scholar 

  46. Reeves, W.T., Blau, R.: Approximate and probabilistic algorithms for shading and rendering structured particle systems. SIGGRAPH Comput. Graph. 19, 313–322 (1985)

    Article  Google Scholar 

  47. Rubin, D.B., Thayer, D.T.: EM algorithms for the ML factor analysis. Psychometrika 47(1), 69–76 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  48. Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 58–63. IEEE Press, New York (2001)

    Google Scholar 

  49. Schodl, A., Szeliski, R., Salesin, D.H., Essa, I.: Video textures. In: ACM SIGGRAPH, pp. 489–498. ACM, New Orleans (2000).

    Chapter  Google Scholar 

  50. Sheikh, Y., Haering, N., Shah, M.: Shape from dynamic texture for planes. In: IEEE Computer Vision and Pattern Recognition or CVPR, pp. 2285–2292 (2006)

    Google Scholar 

  51. Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, vol. 2, pp. 439–446. IEEE Press, Vancouver (2001)

    Google Scholar 

  52. Stam, J., Fiume, E.: Depicting fire and other gaseous phenomena using diffusion processes. Comput. Graph. 29(4), 129–136 (1995)

    Google Scholar 

  53. Szummer, M.: Temporal texture modeling, Ph.D. thesis, TR 346. MIT, Cambridge (1995)

    Google Scholar 

  54. Szummer, M., Pickard, R.: Temporal texture modeling. In: Proc. IEEE Int. Con. Image Processing (ICIP), pp. 823–826. IEEE Press, New York (1996)

    Chapter  Google Scholar 

  55. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  56. Vidal, R.: Identification of spatial-temporal switched ARX systems. In: 48th IEEE Conf. on Decision and Control, pp. 4675–4680 (2007)

    Google Scholar 

  57. Vidal, R., Ravichandran, A.: Optical flow estimation and segmentation of multiple moving dynamic textures. In: CVPR, pp. 516–521. IEEE Comput. Soc., Los Alamitos (2005)

    Google Scholar 

  58. Vidal, R., Soatto, S., Chiuso, A.: Applications of hybrid system identification in computer vision. In: Proc. of European Control Conference (2007)

    Google Scholar 

  59. Vishwanathan, S.V.N., Smola, A.J., Vidal, R.: Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. Int. J. Comput. Vis. 73(1), 95–119 (2007)

    Article  Google Scholar 

  60. Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models. In: NIPS (2005)

    Google Scholar 

  61. Woolfe, F., Fitzgibbon, A.W.: Shift-invariant dynamic texture recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV (2). Lecture Notes in Computer Science, vol. 3952, pp. 549–562. Springer, Berlin (2006)

    Google Scholar 

  62. Xu, L., Sun, H., Jia, J., Tao, C.: Dynamic texture synthesis in the YUV color-space. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds.) Entertainment Computing—ICEC 2007. Lecture Notes in Computer Science, vol. 4740, pp. 243–248. Springer, Berlin (2007)

    Chapter  Google Scholar 

  63. Yellott, J.: Implications of triple correlation uniqueness for texture statistics and the Julesz conjecture. J. Opt. Soc. Am. 10(5), 777–793 (1993)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Inst. of Information Theory & Automation, Acad. of Sciences of the Czech Republic, Prague, Czech Republic

    Michal Haindl

  2. Inst. of Information Theory & Automation, Acad. of Sciences of the Czech Republic, Pod Vodárenskou věží 4, Prague, 182 08, Czech Republic

    Jiří Filip

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  1. Michal Haindl
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  2. Jiří Filip
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Haindl, M., Filip, J. (2013). Dynamic Textures. In: Visual Texture. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4902-6_5

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  • DOI: https://doi.org/10.1007/978-1-4471-4902-6_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4901-9

  • Online ISBN: 978-1-4471-4902-6

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