Foreground detection using loopy belief propagation

Hybrid Loopy Belief Propagation

We propose an algorithm called Hybrid Loopy Belief Propagation (HLBP), which extends the Loopy Belief Propagation (LBP) (Murphy et al., 1999) and Nonparametric Belief Propagation (NBP) (Sudderth et al., 2003) algorithms to deal with general hybrid Bayesian networks. The main idea is to represent the LBP messages with mixture of Gaussians and formulate their calculation as Monte Carlo integratio...

Loopy Belief Propagation Algorithm

check the effectiveness. In some cases, such as Dobrushin's condition is satisfied, the error bounds and the improvement procedure seem effective. Nevertheless, the region where one can obtain good bounds seems restrictive. We give some remarks in the rest of this section. First, the concept of estimates we used in this paper was developed for general Gibbs measures, so that there may be a poss...

Finding Deformable Shapes Using Loopy Belief Propagation

A novel deformable template is presented which detects and localizes shapes in grayscale images. The template is formulated as a Bayesian graphical model of a two-dimensional shape contour, and it is matched to the image using a variant of the belief propagation (BP) algorithm used for inference on graphical models. The algorithm can localize a target shape contour in a cluttered image and can ...

Exact Loopy Belief Propagation on Euler Graphs

Belief propagation is widely used in inference of graphical models. It yields exact solutions when the underlying graph is singly connected. When the graph contains loops, double-counting of evidence may degrade the accuracy of converged beliefs or even prohibit the convergence of messages. In this paper, we propose an exact belief propagation algorithm on Euler graphs. An Euler graph contains ...

The Loopy Belief Propagation for Factor Graphs