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SIAMIS
2010
167views more  SIAMIS 2010»
14 years 9 months ago
Global Solutions of Variational Models with Convex Regularization
Abstract. We propose an algorithmic framework for computing global solutions of variational models with convex regularity terms that permit quite arbitrary data terms. While the mi...
Thomas Pock, Daniel Cremers, Horst Bischof, Antoni...
121
Voted
IJCV
2006
262views more  IJCV 2006»
15 years 2 months ago
A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional
In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Sha...
Xavier Bresson, Pierre Vandergheynst, Jean-Philipp...
ICCV
2001
IEEE
16 years 4 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
84
Voted
ICIP
2003
IEEE
16 years 4 months ago
Adaptive Bayesian networks for video processing
Due to its static nature, the inference capability of Bayesian Networks (BNs) often deteriorates when the basis of input data varies, especially in video processing applications w...
Benny P. L. Lo, Surapa Thiemjarus, Guang-Zhong Yan...
129
Voted
ICIP
2009
IEEE
15 years 8 days ago
A novel two-tier Bayesian based method for hair segmentation
In this paper, a novel two-tier Bayesian based method is proposed for hair segmentation. In the first tier, we construct a Bayesian model by integrating hair occurrence prior prob...
Dan Wang, Shiguang Shan, Wei Zeng, Hongming Zhang,...