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ICPR
2010
IEEE
15 years 4 months ago
Continuous Markov Random Field Optimization using Fusion Move Driven Markov Chain Monte Carlo Technique
Many vision applications have been formulated as Markov Random Field (MRF) problems. Although many of them are discrete labeling problems, continuous formulation often achieves gre...
Wonsik Kim (Seoul National University), Kyoung Mu ...
76
Voted
SAC
2004
ACM
15 years 3 months ago
A comparison of randomized and evolutionary approaches for optimizing base station site selection
It is increasingly important to optimally select base stations in the design of cellular networks, as customers demand cheaper and better wireless services. From a set of potentia...
Larry Raisanen, Roger M. Whitaker, Steve Hurley
65
Voted
ICCV
2007
IEEE
15 years 11 months ago
LogCut - Efficient Graph Cut Optimization for Markov Random Fields
Markov Random Fields (MRFs) are ubiquitous in lowlevel computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to -expansion...
Victor S. Lempitsky, Carsten Rother, Andrew Blake
FOCS
1999
IEEE
15 years 2 months ago
Near-Optimal Conversion of Hardness into Pseudo-Randomness
Various efforts ([?, ?, ?]) have been made in recent years to derandomize probabilistic algorithms using the complexity theoretic assumption that there exists a problem in E = dti...
Russell Impagliazzo, Ronen Shaltiel, Avi Wigderson