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» An Efficient Sequential Monte Carlo Algorithm for Coalescent...
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IPPS
2010
IEEE
13 years 3 months ago
Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs
The Coarse-Grained Monte Carlo (CGMC) method is a multi-scale stochastic mathematical and simulation framework for spatially distributed systems. CGMC simulations are important too...
Lifan Xu, Michela Taufer, Stuart Collins, Dionisio...
CVIU
2006
158views more  CVIU 2006»
13 years 5 months ago
Sequential mean field variational analysis of structured deformable shapes
A novel approach is proposed to analyzing and tracking the motion of structured deformable shapes, which consist of multiple correlated deformable subparts. Since this problem is ...
Gang Hua, Ying Wu
ECCV
2008
Springer
14 years 3 months ago
Window Annealing over Square Lattice Markov Random Field
Monte Carlo methods and their subsequent simulated annealing are able to minimize general energy functions. However, the slow convergence of simulated annealing compared with more ...
Ho Yub Jung, Kyoung Mu Lee, Sang Uk Lee
CVPR
2004
IEEE
14 years 7 months ago
Collaborative Tracking of Multiple Targets
Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targe...
Ting Yu, Ying Wu
ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
13 years 11 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