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ICCV
2009
IEEE
16 years 8 months ago
Adaptive Fragments-Based Tracking of Non-Rigid Objects Using Level Sets
We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature...
Prakash Chockalingam, Nalin Pradeep
CVPR
1999
IEEE
16 years 5 months ago
Explaining Optical Flow Events with Parameterized Spatio-Temporal Models
A spatio-temporal representation for complex optical flow events is developed that generalizes traditional parameterized motion models (e.g. affine). These generative spatio-tempo...
Michael J. Black
CVPR
1999
IEEE
16 years 5 months ago
A Multiple Hypothesis Approach to Figure Tracking
This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is repr...
Tat-Jen Cham, James M. Rehg
CVPR
1999
IEEE
16 years 5 months ago
A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data
We o er a simple paradigm for tting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done ...
Michael Werman, Daniel Keren
CVPR
2001
IEEE
16 years 5 months ago
A Bayesian Approach to Digital Matting
This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel ...
Yung-Yu Chuang, Brian Curless, David Salesin, Rich...