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» Smoothed Particle Filtering for Dynamic Bayesian Networks
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ICDE
2008
IEEE
135views Database» more  ICDE 2008»
14 years 6 months ago
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. he ...
Bhargav Kanagal, Amol Deshpande
ICPR
2004
IEEE
14 years 6 months ago
Switching Particle Filters for Efficient Real-time Visual Tracking
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series signals distributed by non-Gaussian noise. A couple of variant par...
Kenji Doya, Shin Ishii, Takashi Bando, Tomohiro Sh...
ICMLA
2008
13 years 6 months ago
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...
JCB
2006
185views more  JCB 2006»
13 years 5 months ago
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Andrew Golightly, Darren J. Wilkinson
CVPR
2008
IEEE
14 years 7 months ago
Sequential particle swarm optimization for visual tracking
Visual tracking usually involves an optimization process for estimating the motion of an object from measured images in a video sequence. In this paper, a new evolutionary approac...
Xiaoqin Zhang, Weiming Hu, Stephen J. Maybank, Xi ...