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ICPR
2010
IEEE
15 years 8 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 ...
ICCV
2007
IEEE
16 years 3 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 6 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
SPAA
1993
ACM
15 years 6 months ago
Optimal Parallel Construction of Hamiltonian Cycles and Spanning Trees in Random Graphs
We give tight bounds on the parallel complexity of some problems involving random graphs. Speci cally, we show that a Hamiltonian cycle, a breadth rst spanning tree, and a maximal...
Philip D. MacKenzie, Quentin F. Stout