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JCST
2010
139views more  JCST 2010»
13 years 5 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
RC
2007
78views more  RC 2007»
13 years 7 months ago
Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications
Abstract. In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the p...
Vladik Kreinovich, Jan Beck, Carlos Ferregut, Arac...
ECCV
2002
Springer
14 years 9 months ago
A Markov Chain Monte Carlo Approach to Stereovision
We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the d...
Julien Sénégas
ICPR
2006
IEEE
14 years 8 months ago
Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking
In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as ...
Bo Wu, Gérard G. Medioni, Isaac Cohen, Qian...
IJCNN
2006
IEEE
14 years 1 months ago
A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
— Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete e...
Yiwen Wang 0002, António R. C. Paiva, Jose ...