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» A Markov Random Field Model for Medical Image Denoising
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CVPR
2009
IEEE
1382views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Super-Resolution via Recapture and Bayesian Effect Modeling
This paper presents Bayesian edge inference (BEI), a single-frame super-resolution method explicitly grounded in Bayesian inference that addresses issues common to existing meth...
Bryan S. Morse, Dan Ventura, Kevin D. Seppi, Neil ...
ECCV
2004
Springer
16 years 8 months ago
Interactive Image Segmentation Using an Adaptive GMMRF Model
The problem of interactive foreground/background segmentation in still images is of great practical importance in image editing. The state of the art in interactive segmentation is...
Andrew Blake, Carsten Rother, M. Brown, Patrick P&...
ACIVS
2007
Springer
15 years 10 months ago
A Multi-agent Approach for Range Image Segmentation with Bayesian Edge Regularization
Abstract. We present and evaluate in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a rang...
Smaine Mazouzi, Zahia Guessoum, Fabien Michel, Moh...
ICCV
2003
IEEE
16 years 8 months ago
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the i...
Marshall F. Tappen, William T. Freeman
ICCV
2003
IEEE
16 years 8 months ago
A Multi-scale Generative Model for Animate Shapes and Parts
This paper presents a multi-scale generative model for representing animate shapes and extracting meaningful parts of objects. The model assumes that animate shapes (2D simple clo...
Aleksandr Dubinskiy, Song Chun Zhu