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UAI
2000
13 years 6 months ago
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
JUCS
2006
107views more  JUCS 2006»
13 years 5 months ago
Sequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data
: Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which...
Kazuyuki Nakamura, Tomoyuki Higuchi, Naoki Hirose
FGR
2004
IEEE
105views Biometrics» more  FGR 2004»
13 years 9 months ago
Particle Filtering with Factorized Likelihoods for Tracking Facial Features
In the recent years particle filtering has been the dominant paradigm for tracking facial and body features, recognizing temporal events and reasoning in uncertainty. A major prob...
Ioannis Patras, Maja Pantic
CVPR
2003
IEEE
14 years 7 months ago
Nonparametric Belief Propagation
In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There...
Erik B. Sudderth, Alexander T. Ihler, William T. F...
ICPR
2008
IEEE
13 years 11 months ago
Tracking human body by using particle filter Gaussian process Markov-switching model
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...
Jing Wang, Hong Man, Yafeng Yin