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ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
14 years 1 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on t...
Yu Huang, Joan Llach
ICML
2005
IEEE
14 years 7 months ago
Learning Gaussian processes from multiple tasks
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equival...
Kai Yu, Volker Tresp, Anton Schwaighofer
VISUAL
2000
Springer
13 years 10 months ago
Statistical Motion-Based Retrieval with Partial Query
We present an original approach for motion-based retrieval involving partial query. More precisely, we propose an uni ed statistical framework both to extract entities of interest ...
Ronan Fablet, Patrick Bouthemy
ICIP
2005
IEEE
14 years 8 months ago
Bayesian visual tracking with existence process
Most object tracking approaches either assume that the number of objects is constant, or that information about object existence is provided by some external source. Here, we show...
Jaco Vermaak, Mark Briers, Patrick Pérez, S...
NIPS
1997
13 years 8 months ago
Graph Matching with Hierarchical Discrete Relaxation
Our aim in this paper is to develop a Bayesian framework for matching hierarchical relational models. Such models are widespread in computer vision. The framework that we adopt fo...
Richard C. Wilson, Edwin R. Hancock