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.
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Patrick Bouthemy graduated from ENST, Paris, in 1980, and received the Ph.D degree in Computer Science from the University of Rennes, France, in 1982. From December 1982 until February 1984, he was employed by INRS-Telecommunications, Montreal, in the Department of Visual Communications. Since April 1984 he has been with INRIA, at IRISA in Rennes. He is currently“Directeur de Recherche” Inria and head of Vista group. His main research interests are: statistical approaches for image sequence processing, motion analysis, learning, recognition and classification of dynamic contents in video. He has served as member of the program committees of the major conferences in image processing and computer vision. He was Associate Editor of the IEEE Transactions on Image Processing from 1999 to 2003.
Cécile Hardouin received the Ph.D degree in Applied Mathematics from University Paris 7, France, in 1992. Since July 1992 she is lecturer at University Paris 10, Nanterre. Since her Ph.D., she is member of the research center SAMOS - MATISSE, located in University Paris 1. After some works on long memory processes, her main research interests are now Spatial statistics: Markovian random fields, standards adoption dynamics, spatial coordination.
Gwenaëlle Piriou was born in 1976. She received the Ph.D. degree in signal processing and telecommunications from the university of Rennes, France, in 2005. She is currently an assistant professor with the computer science department of university of Rennes. Her main research interests are probabilistic motion modeling in image sequence, recognition and detection of dynamic content and temporal textures.
Jian-feng Yao recieved the Ph.D. degree in Applied Mathematics from Université Paris-Sud (Orsay), France, in 1990. From January 1990 to August 2000, he was a Maître de Conference in University of Paris I. Since September 2000, he is a full professor in Statistics in University of Rennes I. His current research interests are nonlinear time series analysis, spectral theory of large-dimensional random matrices and mathematical modeling for image understanding.
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Bouthemy, P., Hardouin, C., Piriou, G. et al. Mixed-State Auto-Models and Motion Texture Modeling. J Math Imaging Vis 25, 387–402 (2006). https://doi.org/10.1007/s10851-006-7251-1
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DOI: https://doi.org/10.1007/s10851-006-7251-1