Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Second International Edwin R. Hancock,Marcello Pelillo Conference p

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Metropolis vs Kawasaki Dynamic for Image Segmentation Based on Gibbs Modelsre only an estimation of the class ratios. We show on synthetic data that these new dynamics can improve the segmentation results by incorporating some information on the class ratios. Results are compared using a Potts model as prior distribution.

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Auxiliary Variables for Markov Random Fields with Higher Order Interactionspeed-up to computation with the simple Ising and Potts models. In this paper we show how the same ideas can be used with binary Markov Random Fields with essentially any support to construct auxiliary variable algorithms. However, because of the complexity and certain characteristics of the models, the computational gains are limited.

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Bayesian A* Tree Search with Expected O(N) Convergence Rates for Road Trackingty distribution on the ensemble of problem instances, which we call the .. We analyze the Bayesian ensemble, using techniques from information theory, and mathematically prove expected O(N) convergence rates of inadmissible A. algorithms. These rates depend on an “order parameter” which characterizes the difficulty of the problem.

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Convergence of a Hill Climbing Genetic Algorithm for Graph Matching behaviour. The main conclusion of this study is that the hill-climbing step significantly accelerates convergence, and that the convergence rate is polynomial in the size of the node-set of the graphs being matched.

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