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» Supervised Image Segmentation Using Markov Random Fields
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ICCV
2009
IEEE
16 years 2 months ago
Higher-Order Gradient Descent by Fusion-Move Graph Cut
Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higherorder graph...
Hiroshi Ishikawa
IVC
2008
138views more  IVC 2008»
14 years 9 months ago
Reconstructing relief surfaces
This paper generalizes Markov Random Field (MRF) stereo methods to the generation of surface relief (height) fields rather than disparity or depth maps. This generalization enable...
George Vogiatzis, Philip H. S. Torr, Steven M. Sei...
CVPR
2009
IEEE
16 years 4 months ago
Higher-Order Clique Reduction in Binary Graph Cut
We introduce a new technique that can reduce any higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, w...
Hiroshi Ishikawa 0002
CVPR
2005
IEEE
15 years 11 months ago
Digital Tapestry
This paper addresses the novel problem of automatically synthesizing an output image from a large collection of different input images. The synthesized image, called a digital tap...
Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov,...
ICCV
2009
IEEE
16 years 2 months ago
Boundary Ownership by Lifting to 2.5D
This paper addresses the “boundary ownership” problem, also known as the figure/ground assignment problem. Estimating boundary ownerships is a key step in perceptual organiz...
Ido Leichter and Michael Lindenbaum