Mixed-State Auto-Models and Motion Texture Modeling

Authors:
P. Bouthemy;C. Hardouin;G. Piriou;J. Yao
Affiliations:
IRISA/INRIA, Rennes Cedex, France 35042;SAMOS/Université de Paris 1, Paris Cedex 13, France 75634;IRISA/INRIA, Rennes Cedex, France 35042;IRMAR/Université de Rennes 1, Rennes Cedex, France 35042
Venue:
Journal of Mathematical Imaging and Vision
Year:
2006

Citing 3
Cited 7

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Abstract

In image motion analysis as well as for several application fields like daily pluviometry data modeling, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". In this work we introduce a generalization of Besag's auto-models to deal with mixed-state observations at each site of a lattice. A careful construction as well as important properties of the model will be given. A special class of positive Gaussian mixed-state auto-models is proposed for the analysis of motion textures from video sequences. This model is first explored via simulations. We then apply it to real images of dynamic natural scenes.