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» Modeling Image Textures by Gibbs Random Fields
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97
Voted
ICIP
2010
IEEE
14 years 10 months ago
View synthesis based on Conditional Random Fields and graph cuts
We propose a novel method to synthesize intermediate views from two stereo images and disparity maps that is robust to errors in disparity map. The proposed method computes a plac...
Lam C. Tran, Christopher J. Pal, Truong Q. Nguyen
114
Voted
MICCAI
2003
Springer
16 years 1 months ago
Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach
Abstract. In this paper we propose a novel non-Gaussian MRF for regularization of tensor fields for fiber tract enhancement. Two entities are considered in the model, namely, the l...
Marcos Martín-Fernández, Carlos Albe...
154
Voted
ICDAR
2009
IEEE
14 years 10 months ago
Unconstrained Handwritten Document Layout Extraction Using 2D Conditional Random Fields
The paper describes a new approach using a Conditional Random Fields (CRFs) to extract physical and logical layouts in unconstrained handwritten letters such as those sent by indi...
Florent Montreuil, Emmanuele Grosicki, Laurent Heu...
ICIP
2010
IEEE
14 years 10 months ago
3D augmented Markov random field for object recognition
In this paper, we propose to use 3D information to augment the Markov random field (MRF) model for object recognition. Conventional MRF for image-based object recognition usually ...
Wei Yu, Ahmed Bilal Ashraf, Yao-Jen Chang, Congcon...
95
Voted
CVPR
2007
IEEE
16 years 2 months ago
Learning Conditional Random Fields for Stereo
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for e...
Daniel Scharstein, Chris Pal