Temporal modeling of motion textures with mixed-sates Markov chains

Dynamic textures are time-varying visual patterns that exhibit certain spatio-temporal stationarity properties and are displayed mostly by natural scene elements. In this paper, we present new statistical models for the characterization of motion in this type of sequences. First we observe that motion measurements present values of two types: a discrete component at zero expressing the absence of motion and a continuous distribution for the rest of the motion values. Thus, we define random variables with mixed-states and propose to model a sequence of motion maps as a Markov chain, where the transition densities are mixed-state probability densities. Based on this approach, we propose a method for dynamic texture segmentation in real sequences showing the efficiency of the proposal in dynamic content analysis applications.