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CVPR
2007
IEEE
16 years 1 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...
CVPR
2010
IEEE
15 years 8 months ago
Vessel Scale Selection using MRF Optimization
Many feature detection algorithms rely on the choice of scale. In this paper, we complement standard scaleselection algorithms with spatial regularization. To this end, we formula...
Hengameh Mirzaalian, Ghassan Hamarneh
IJCV
2006
161views more  IJCV 2006»
14 years 11 months ago
Discriminative Random Fields
In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and mode...
Sanjiv Kumar, Martial Hebert
ECCV
2008
Springer
16 years 1 months ago
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...
Jerod J. Weinman, Lam Tran, Christopher J. Pal
ICPR
2008
IEEE
16 years 1 months ago
HOPS: Efficient region labeling using Higher Order Proxy Neighborhoods
We present the Higher Order Proxy Neighborhoods (HOPS) approach to modeling higher order neighborhoods in Markov Random Fields (MRFs). HOPS incorporates more context information i...
Albert Y. C. Chen, Jason J. Corso, Le Wang