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IJAR
2007
69views more  IJAR 2007»
15 years 6 months ago
Decision making under uncertainty using imprecise probabilities
Various ways for decision making with imprecise probabilities—admissibility, maximal expected utility, maximality, E-admissibility, Γ-maximax, Γ-maximin, all of which are well...
Matthias C. M. Troffaes
AAAI
2006
15 years 8 months ago
Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning
Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world proble...
Shimon Whiteson, Peter Stone
CVPR
2006
IEEE
16 years 8 months ago
Stereo Matching with Symmetric Cost Functions
Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various ...
Kuk-Jin Yoon, In-So Kweon
TIP
1998
162views more  TIP 1998»
15 years 6 months ago
Variational image segmentation using boundary functions
Abstract—A general variational framework for image approximation and segmentation is introduced. By using a continuous “line-process” to represent edge boundaries, it is poss...
Gary A. Hewer, Charles S. Kenney, B. S. Manjunath
ECCV
2002
Springer
16 years 8 months ago
What Energy Functions Can Be Minimized via Graph Cuts?
In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs...
Vladimir Kolmogorov, Ramin Zabih