Mixed-state Markov models in image motion analysis

Abstract : When analyzing motion observations extracted from image sequences, one notes that the histogram of the velocity magnitude at each pixel shows a large probability mass at zero velocity, while the rest of the motion values may be appropriately modeled with a continuous distribution. This suggests the introduction of mixed-state random variables that have probability mass concentrated in discrete states, while they have a probability density over a continuous range of values. In the first part of the chapter, we give a comprehensive description of the theory behind mixed-state statistical models, in particular the development of mixed-state Markov models that permits to take into account spatial and temporal interaction. The presentation generalizes the case of simultaneous modeling of continuous values and any type of discrete symbolic states. For the second part, we present the application of mixed-state models to motion texture analysis. Motion textures correspond to the instantaneous apparent motion maps extracted from dynamic textures. They depict mixed-state motion values with a discrete state at zero and a Gaussian distribution for the rest. Mixed-state Markov random fields and mixed-state Markov chains are defined and applied to motion texture recognition and tracking.
Type de document :
Chapitre d'ouvrage
Springer. Machine Learning for Vision-Based Motion Analysis, 1st Edition., 2011, XIV, 372 pages, SpringerLink, pp.Part 2, 77-115, 2011, Theory and Techniques Series: Advances in Pattern Recognition / Wang, L.; Zhao, G.; Cheng, L.; Pietikäinen, M. (Eds.), 978-0-85729-056-4. 〈10.1007/978-0-85729-057-1_4〉
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https://hal.inria.fr/inria-00541253
Contributeur : Charles Kervrann <>
Soumis le : mardi 30 novembre 2010 - 11:25:15
Dernière modification le : jeudi 11 janvier 2018 - 06:12:24

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Tomas Crivelli, Bruno Cernuschi-Frias, Patrick Bouthemy, Jian-Feng Yao. Mixed-state Markov models in image motion analysis. Springer. Machine Learning for Vision-Based Motion Analysis, 1st Edition., 2011, XIV, 372 pages, SpringerLink, pp.Part 2, 77-115, 2011, Theory and Techniques Series: Advances in Pattern Recognition / Wang, L.; Zhao, G.; Cheng, L.; Pietikäinen, M. (Eds.), 978-0-85729-056-4. 〈10.1007/978-0-85729-057-1_4〉. 〈inria-00541253〉

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