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» A probabilistic framework for image segmentation
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ICPR
2000
IEEE
13 years 11 months ago
MRF Solutions for Probabilistic Optical Flow Formulations
In this paper we propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncer...
Sébastien Roy, Venu Govindu
ICCV
2001
IEEE
14 years 10 months ago
Learning Image Statistics for Bayesian Tracking
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
Hedvig Sidenbladh, Michael J. Black
MICCAI
1998
Springer
14 years 5 days ago
Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures
We propose a new way of embedding shape distributions in a topological deformable template. These distributions rely on global shape descriptors corresponding to the 3D moment inva...
Fabrice Poupon, Jean-Francois Mangin, Dominique Ha...
ICIP
2004
IEEE
14 years 9 months ago
A probabilistic cooperation between trackers of coupled objects
Much work has been done in the field of visual object tracking, yielding a wide range of trackers, including ones aimed for multiple objects. In many cases, there may be a couplin...
Ido Leichter, Michael Lindenbaum, Ehud Rivlin
TMM
2002
104views more  TMM 2002»
13 years 7 months ago
Spatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...