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IBPRIA
2005
Springer
13 years 9 months ago
Probabilistic Image-Based Tracking: Improving Particle Filtering
Condensation is a widely-used tracking algorithm based on particle filters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highl...
Daniel Rowe, Ignasi Rius, Jordi Gonzàlez, F...
ISBI
2007
IEEE
13 years 10 months ago
Advanced Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images
Quantitative analysis of dynamical processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. D...
Ihor Smal, Wiro J. Niessen, Erik H. W. Meijering
ECCV
2000
Springer
14 years 6 months ago
A Probabilistic Background Model for Tracking
A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. Th...
Jens Rittscher, Jien Kato, Sébastien Joga, ...
ICIP
2005
IEEE
13 years 10 months ago
Improving particle filter with support vector regression for efficient visual tracking
—Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density...
Guangyu Zhu, Dawei Liang, Yang Liu, Qingming Huang...
IPMI
2007
Springer
14 years 5 months ago
Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging
Modern live cell fluorescence microscopy imaging systems, used abundantly for studying intra-cellular processes in vivo, generate vast amounts of noisy image data that cannot be pr...
Ihor Smal, Katharina Draegestein, Niels Galjart, W...