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» Fusion Moves for Markov Random Field Optimization
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CVPR
2010
IEEE
1790views Computer Vision» more  CVPR 2010»
14 years 4 months ago
Data Driven Mean-Shift Belief Propagation For non-Gaussian MRFs
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...
Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
NIPS
2007
13 years 10 months ago
New Outer Bounds on the Marginal Polytope
We give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concav...
David Sontag, Tommi Jaakkola
MICCAI
2010
Springer
13 years 7 months ago
Simultaneous Geometric - Iconic Registration
In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulat...
Aristeidis Sotiras, Yangming Ou, Ben Glocker, Chri...
CVPR
2012
IEEE
11 years 11 months ago
A tiered move-making algorithm for general pairwise MRFs
A large number of problems in computer vision can be modeled as energy minimization problems in a markov random field (MRF) framework. Many methods have been developed over the y...
Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
ICCV
2009
IEEE
15 years 1 months ago
A Global Perspective on MAP Inference for Low-Level Vision
In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, ...
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo...