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» Multiscale Conditional Random Fields for Image Labeling
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BMVC
2010
13 years 6 months ago
Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction
The problems of dense stereo reconstruction and object class segmentation can both be formulated as Conditional Random Field based labelling problems, in which every pixel in the ...
Lubor Ladicky, Paul Sturgess, Christopher Russell,...
ECCV
2006
Springer
14 years 10 months ago
Learning and Incorporating Top-Down Cues in Image Segmentation
Abstract. Bottom-up approaches, which rely mainly on continuity principles, are often insufficient to form accurate segments in natural images. In order to improve performance, rec...
Xuming He, Richard S. Zemel, Debajyoti Ray
CVPR
2008
IEEE
14 years 10 months ago
Auto-context and its application to high-level vision tasks
The notion of using context information for solving highlevel vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context...
Zhuowen Tu
ICML
2007
IEEE
14 years 9 months ago
Dynamic hierarchical Markov random fields and their application to web data extraction
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In...
Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen
ICIP
2005
IEEE
14 years 10 months ago
A segmentation method using compound Markov random fields based on a general boundary model
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a ...
Jue Wu, Albert C. S. Chung