Auto-models with mixed states and analysis of motion textures
Patrick Bouthémy, Cécile Hardouin, Gwénaëlle Piriou, Jianfeng Yao · HAL (Le Centre pour la Communication Scientifique Directe) · 2005
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. The performance of the model is then evaluated on the modeling of motion textures from video sequences.