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Partitioned Sampling, Articulated Objects, and interfacequality hand tracking
, 2000
"... This paper describes how to use partitioned sampling on articulated objects, obtaining results that would be impossible with standard sampling methods. Because partitioned sampling is the statistical analogue of a hierarchical search, it makes sense to use it on articulated objects, since links at t ..."
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Cited by 173 (3 self)
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This paper describes how to use partitioned sampling on articulated objects, obtaining results that would be impossible with standard sampling methods. Because partitioned sampling is the statistical analogue of a hierarchical search, it makes sense to use it on articulated objects, since links at the base of the object can be localised before moving on to search for subsequent links
Mixture Kalman filters
, 2000
"... In treating dynamic systems,sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the online `filtering' task. We propose a special sequential Monte Carlo method,the ..."
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Cited by 151 (5 self)
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In treating dynamic systems,sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the online `filtering' task. We propose a special sequential Monte Carlo method,the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for online estimation and prediction of conditional and partial conditional dynamic linear models,which are themselves a class of widely used nonlinear systems and also serve to approximate many others. Compared with a few available filtering methods including Monte Carlo methods,the gain in efficiency that is provided by the mixture Kalman filter can be very substantial. Another contribution of the paper is the formulation of many nonlinear systems into conditional or partial conditional linear form,to which the mixture Kalman filter can be applied. Examples in target tracking and digital communications are given to demonstrate the procedures proposed.
Visual Motion Analysis by Probabilistic Propagation of Conditional Density
, 1998
"... This thesis establishes a stochastic framework for tracking curves in visual clutter, using a Bayesian randomsampling algorithm. The approach is rooted in ideas from statistics, control theory and computer vision. The problem is to track outlines and features of foreground objects, modelled as curv ..."
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Cited by 28 (0 self)
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This thesis establishes a stochastic framework for tracking curves in visual clutter, using a Bayesian randomsampling algorithm. The approach is rooted in ideas from statistics, control theory and computer vision. The problem is to track outlines and features of foreground objects, modelled as curves, as they move in substantial clutter, and to do it at, or close to, video framerate. The algorithm, named Condensation, for Conditional density propagation, has recently been derived independently by several researchers, and is generating signi cant interest in the statistics and signal processing communities. This thesis contributes to the literature on Condensationlike lters by presenting some novel applications of and extensions to the basic algorithm, and contributes to the visual motion estimation literature by demonstrating high tracking performance in cluttered environments. Despite its power the Condensation algorithm has a remarkably simple form and this allows the use of nonlinear motion models which combine characteristics of discrete Hidden Markov Models with the continuous AutoRegressive Process motion models traditionally used in Kalman lters. These mixed discretecontinuous models have promising applications to the emerging eld of perception of action. This thesis also implements two algorithms to smooth the output of the Condensation lter which improves the accuracy of motion estimation in a batchmode procedure after tracking is complete.
Auxiliary Variable Based Particle Filters
, 1999
"... Introduction We model a time series fy t ;t=1; :::; ng using a state space framework with the fy t j# t g being independent and with the state f# t g assumed to be Markovian. The task will be to use simulation to estimate f## t jF t #, t =1; :::; n, where F t is contemporaneously available informa ..."
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Cited by 24 (2 self)
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Introduction We model a time series fy t ;t=1; :::; ng using a state space framework with the fy t j# t g being independent and with the state f# t g assumed to be Markovian. The task will be to use simulation to estimate f## t jF t #, t =1; :::; n, where F t is contemporaneously available information. We assume a known `measurement' density f#y t j# t # and the ability to simulate from the `transition' density f## t+1 j# t #. Sometimes we will also assume that we can evaluate f## t+1 j# t #. Filtering can be th
Efficient Bayesian Inference for Switching StateSpace Models using Particle Markov Chain Monte Carlo Methods
, 2010
"... Switching statespace models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated ..."
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Cited by 6 (0 self)
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Switching statespace models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated MCMC methods dedicated to SSSM can prove quite inefficient as they update potentially strongly correlated variables oneatatime. Particle Markov chain Monte Carlo (PMCMC) methods are a recently developed class of MCMC algorithms which use particle filters to build efficient proposal distributions in highdimensions [1]. The existing PMCMC methods of [1] are applicable to SSSM, but are restricted to employing standard particle filtering techniques. Yet, in the context of SSSM, much more efficient particle techniques have been developed [22, 23, 24]. In this paper, we extend the PMCMC framework to enable the use of these efficient particle methods within MCMC. We demonstrate the resulting generic methodology on a variety of examples including a multiple changepoints model for welllog data and a model for U.S./U.K. exchange rate data. These new PMCMC algorithms are shown to outperform experimentally stateoftheart MCMC techniques for a fixed computational complexity. Additionally they can be easily parallelized [39] which allows further substantial gains.
Selforganizing time series model
 Sequential Monte Carlo Methods in Practice
, 2001
"... 1.1 Generalized state space model The generalized state space model (GSSM) that we deal with in this study is de ned by a set of two equations system model xt = f(xt;1 � vt) � and (1.1) ..."
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Cited by 4 (0 self)
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1.1 Generalized state space model The generalized state space model (GSSM) that we deal with in this study is de ned by a set of two equations system model xt = f(xt;1 � vt) � and (1.1)