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65
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ICCV
2007
IEEE
15 years 11 months ago
LogCut - Efficient Graph Cut Optimization for Markov Random Fields
Markov Random Fields (MRFs) are ubiquitous in lowlevel computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to -expansion...
Victor S. Lempitsky, Carsten Rother, Andrew Blake
72
Voted
ICIP
2005
IEEE
15 years 11 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
74
Voted
ICIP
2003
IEEE
15 years 11 months ago
Object localization using texture motifs and Markov random fields
This work presents a novel approach to object localization in complex imagery. In particular, the spatial extents of objects characterized by distinct spatial signatures at multip...
Shawn Newsam, Sitaram Bhagavathy, B. S. Manjunath
TOG
2008
103views more  TOG 2008»
14 years 9 months ago
Sketch-based tree modeling using Markov random field
In this paper, we describe a new system for converting a user's freehand sketch of a tree into a full 3D model that is both complex and realistic-looking. Our system does thi...
Xuejin Chen, Boris Neubert, Ying-Qing Xu, Oliver D...
90
Voted
CVPR
2007
IEEE
15 years 11 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen