Sciweavers

Share
CVPR
2004
IEEE

Modeling Complex Motion by Tracking and Editing Hidden Markov Graphs

10 years 7 months ago
Modeling Complex Motion by Tracking and Editing Hidden Markov Graphs
In this paper, we propose a generative model for representing complex motion, such as wavy river, dancing fire and dangling cloth. Our generative method consists of four components: (1) A photometric model using primal sketch[8] which transfers an image into an attribute graph representation. Each vertex of the graph is a scaled and oriented image patch selected from a dictionary. The graph connects and aligns these patches. (2) A geometric model which characterizes the deformation of the attribute graph. (3) A dynamic model, which specifies the motion dynamics of these vertices (patches) and their interactions in the form of coupled Markov chains. (4) A topological model, which interprets the graph topological changes over time. We learn this generative model by a stochastic gradient algorithm implemented by Markov Chain Monte Carlo (MCMC) sampling. This method is shown to be effective in handling the topological changes of graphs. The correctness of the learned model is verified by ...
Yizhou Wang, Song Chun Zhu
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CVPR
Authors Yizhou Wang, Song Chun Zhu
Comments (0)
books