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» Markov Random Field Models in Computer Vision
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ECCV
2002
Springer
14 years 10 months ago
Finding the Largest Unambiguous Component of Stereo Matching
Abstract. Stereo matching is an ill-posed problem for at least two principal reasons: (1) because of the random nature of match similarity measure and (2) because of structural amb...
Radim Sára
ICPR
2008
IEEE
14 years 9 months ago
A novel Gaussianized vector representation for natural scene categorization
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of featur...
Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang, ...
RECOMB
2007
Springer
14 years 8 months ago
Minimizing and Learning Energy Functions for Side-Chain Prediction
Abstract. Side-chain prediction is an important subproblem of the general protein folding problem. Despite much progress in side-chain prediction, performance is far from satisfact...
Chen Yanover, Ora Schueler-Furman, Yair Weiss
ACL
2010
13 years 6 months ago
Practical Very Large Scale CRFs
Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate str...
Thomas Lavergne, Olivier Cappé, Franç...
ECCV
2008
Springer
14 years 10 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...