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» Markov Random Fields with Efficient Approximations
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UAI
2004
13 years 9 months ago
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Iain Murray, Zoubin Ghahramani
ICCV
2009
IEEE
15 years 14 days ago
A Probabilistic Framework for Partial Intrinsic Symmetries in Geometric Data
In this paper, we present a novel algorithm for partial intrinsic symmetry detection in 3D geometry. Unlike previous work, our algorithm is based on a conceptually simple and st...
Ruxandra Lasowski, Art Tevs, Hans-Peter Seidel, Mi...
CVPR
2006
IEEE
14 years 1 months ago
Combined Depth and Outlier Estimation in Multi-View Stereo
In this paper, we present a generative model based approach to solve the multi-view stereo problem. The input images are considered to be generated by either one of two processes:...
Christoph Strecha, Rik Fransens, Luc J. Van Gool
CORR
2010
Springer
174views Education» more  CORR 2010»
13 years 7 months ago
Hybrid Numerical Solution of the Chemical Master Equation
We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the ...
Thomas A. Henzinger, Maria Mateescu, Linar Mikeev,...
CVPR
2007
IEEE
14 years 9 months ago
Efficient Belief Propagation for Vision Using Linear Constraint Nodes
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems....
Brian Potetz