Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation

@article{Crivelli2006MixedStateMR,
  title={Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation},
  author={Tom{\'a}s Crivelli and Bruno Cernuschi-Fr{\'i}as and Patrick Bouthemy and Jian-Feng Yao},
  journal={2006 International Conference on Image Processing},
  year={2006},
  pages={1857-1860}
}
The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov random fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative… CONTINUE READING
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Bouthemy , " Motion recognition using non - parametric image motion models estimated from temporal and multiscale coocurrence statistics

  • R. Fablet, P.
  • Pattern Analysis and Machine Intelligence

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