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ICIP
2003
IEEE
14 years 6 months ago
A probabilistic framework for image segmentation
A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hyp...
Slawo Wesolkowski, Paul W. Fieguth
3DOR
2008
13 years 7 months ago
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
Guillaume Lavoué, Christian Wolf
MICCAI
2005
Springer
14 years 5 months ago
Cross Entropy: A New Solver for Markov Random Field Modeling and Applications to Medical Image Segmentation
This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulati...
Jue Wu, Albert C. S. Chung
CVPR
2007
IEEE
14 years 6 months ago
Multi-label image segmentation via max-sum solver
We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recent...
Branislav Micusík, Tomás Pajdla
CVPR
2008
IEEE
14 years 6 months ago
Combining appearance models and Markov Random Fields for category level object segmentation
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider obje...
Diane Larlus, Frédéric Jurie