Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
Tomás Crivelli, Bruno Cernuschi-Frías, Patrick Bouthémy, Jianfeng Yao · 2006
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 extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.