We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...
In this paper, we study the notion of marginal independence between two sets of variables when uncertainty is expressed by belief functions as understood in the context of the tra...
Boutheina Ben Yaghlane, Philippe Smets, Khaled Mel...
We propose a new approach to value-directed belief state approximationfor POMDPs. The valuedirected model allows one to choose approximation methods for belief state monitoringtha...
We propose a novel statistical method for motion detection and background maintenance for a mobile observer. Our method is based on global motion estimation and statistical backgro...
Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffus...
Siming Wei, Jing Hua, Jiajun Bu, Chun Chen, Yizhou...