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» Structure From Motion Using Sequential Monte Carlo Methods
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TASLP
2002
109views more  TASLP 2002»
13 years 7 months ago
Particle methods for Bayesian modeling and enhancement of speech signals
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
Jaco Vermaak, Christophe Andrieu, Arnaud Doucet, S...
JMLR
2010
163views more  JMLR 2010»
13 years 2 months ago
Active Sequential Learning with Tactile Feedback
We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and highdimensional...
Hannes Saal, Jo-Anne Ting, Sethu Vijayakumar
ICIP
2003
IEEE
14 years 9 months ago
Robust tracking of cyclic nonrigid motion
Cyclic motion underlies several human activities including exercising,running, and walking. Accurate tracking of such motion in video data helps in developing computer-aided appli...
Cheng Chang, Rashid Ansari, Ashfaq A. Khokhar
ISBI
2006
IEEE
14 years 8 months ago
Bayesian tracking for fluorescence microscopic imaging
Fluorescence microscopy is a powerful imaging tool for studying molecular dynamics in living cells. For quantitative motion analysis of subcellular structures robust and accurate ...
Ihor Smal, Wiro J. Niessen, Erik H. W. Meijering
ICIP
2002
IEEE
14 years 9 months ago
A Bayesian approach to inferring vascular tree structure from 2D imagery
We describe a method for inferring tree-like vascular structures from 2D imagery. A Markov Chain Monte Carlo (MCMC) algorithm is employed to produce approximate samples from the p...
Abhir Bhalerao, Elke Thönnes, Roland Wilson, ...