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European Conference on Computer Vision

ECCV 2002: Computer Vision — ECCV 2002 pp 583–598Cite as

A Generative Method for Textured Motion: Analysis and Synthesis

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2350)

Abstract

Natural scenes contain rich stochastic motion patterns which are characterized by the movement of a large number of small elements, such as falling snow, raining, flying birds, firework and waterfall. In this paper, we call these motion patterns textured motion and present a generative method that combines statistical models and algorithms from both texture and motion analysis. The generative method includes the following three aspects. 1). Photometrically, an image is represented as a superposition of linear bases in atomic decomposition using an over-complete dictionary, such as Gabor or Laplacian. Such base representation is known to be generic for natural images, and it is low dimensional as the number of bases is often 100 times smaller than the number of pixels. 2). Geometrically, each moving element (called moveton), such as the individual snowflake and bird, is represented by a deformable template which is a group of several spatially adjacent bases. Such templates are learned through clustering. 3). Dynamically, the movetons are tracked through the image sequence by a stochastic algorithm maximizing a posterior probability. A classic second order Markov chain model is adopted for the motion dynamics. The sources and sinks of the movetons are modeled by birth and death maps. We adopt an EM-like stochastic gradient algorithm for inference of the hidden variables: bases, movetons, birth/death maps, parameters of the dynamics. The learned models are also verified through synthesizing random textured motion sequences which bear similar visual appearance with the observed sequences.

Keywords

  • Motion Dynamic
  • Markov Chain Model
  • Atomic Decomposition
  • Texture Synthesis
  • Texture Modeling

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.

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Author information

Authors and Affiliations

  1. Dept. of Comp. and Info. Sci., Ohio State Univ., Columbus, OH, 43210, USA

    Yizhou Wang & Song-Chun Zhu

Authors
  1. Yizhou Wang
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  2. Song-Chun Zhu
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Editor information

Editors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Box 118, 22100, Lund, Sweden

    Anders Heyden & Gunnar Sparr & 

  2. The IT University of Copenhagen, Glentevej 67-69, 2400, Copenhagen, NW, Denmark

    Mads Nielsen

  3. University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark

    Peter Johansen

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© 2002 Springer-Verlag Berlin Heidelberg

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Wang, Y., Zhu, SC. (2002). A Generative Method for Textured Motion: Analysis and Synthesis. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47969-4_39

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  • DOI: https://doi.org/10.1007/3-540-47969-4_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43745-1

  • Online ISBN: 978-3-540-47969-7

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