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» A Bayesian Framework for Multi-cue 3D Object Tracking
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SSIAI
2000
IEEE
13 years 12 months ago
A New Bayesian Relaxation Framework for the Estimation and Segmentation of Multiple Motions
In this paper we propose a new probabilistic relaxation framework to perform robust multiple motion estimation and segmentation from a sequence of images. Our approach uses displa...
Alexander Strehl, Jake K. Aggarwal
CVPR
2006
IEEE
14 years 9 months ago
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using...
Alexandru O. Balan, Michael J. Black
ICCV
2001
IEEE
14 years 9 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
2004
Springer
14 years 8 months ago
Profile Scale-Spaces for Multiscale Image Match
Anatomical objects often have complex and varying image appearance at different portions of the boundary; and it is frequently a challenge even to select appropriate scales at whic...
Sean Ho, Guido Gerig
ICASSP
2008
IEEE
14 years 1 months ago
A factorization method in stereo motion for non-rigid objects
In this paper we propose a framework of factorization-based non-rigid shape modeling and tracking in stereo-motion. We construct a measurement matrix with the stereo-motion data c...
Yu Huang, Jilin Tu, Thomas S. Huang