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62
Dynamic Textures
, 2002
"... Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing ..."
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Cited by 295 (15 self)
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Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the "essence" of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of secondorder stationary processes, we identify the model suboptimally in closedform. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even lowdimensional models can capture very complex visual phenomena.
The Unscented Particle Filter
, 2000
"... In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available info ..."
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Cited by 163 (9 self)
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In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.
Capturing Natural Hand Articulation
 In ICCV
, 2001
"... Visionbased motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Modelbased approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and imag ..."
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Cited by 93 (10 self)
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Visionbased motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Modelbased approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a modelbased method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of linear manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motioncapturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
Monte Carlo Methods for Tempo Tracking and Rhythm Quantization
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2003
"... We present a probabilistic generarive model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables ..."
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Cited by 56 (9 self)
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We present a probabilistic generarive model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Ex act computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.
Tracking Articulated Body by Dynamic Markov Network
 PROC. IEEE INT'L CONF. ON COMPUTER VISION, NICE, FRANCE
, 2003
"... A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendous increase of computation. To ease this problem, we propose an approach that analyzes subparts locally while reinforcing ..."
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Cited by 55 (9 self)
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A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendous increase of computation. To ease this problem, we propose an approach that analyzes subparts locally while reinforcing the structural constraints at the mean time. The computational model of the proposed approach is based on a dynamic Markov network, a generative model which characterizes the dynamics and the image observations of each individual subpart as well as the motion constraints among different subparts. Probabilistic variational analysis of the model reveals a mean field approximation to the posterior densities of each subparts given visual evidence, and provides a computationally efficient way for such a difficult Bayesian inference problem. In addition, we design mean field Monte Carlo (MFMC) algorithms, in which a set of low dimensional particle filters interact with each other and solve the high dimensional problem collaboratively. Extensive experiments on tracking human body parts demonstrate the effectiveness, significance and computational efficiency of the proposed method.
Particle Filters for State Space Models With the Presence of Static Parameters
, 2002
"... In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be ..."
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Cited by 49 (0 self)
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In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, realtime applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some lowdimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state space models. Marginalizing the static parameters avoids the problem of impoverishment which typically occur when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.
Regularized stochastic white matter tractography using diffusion tensor mri
 In MICCAI
, 2002
"... Abstract. The development of Diffusion Tensor MRI has raised hopes in the neuroscience community for in vivo methods to track fiber paths in the white matter. A number of approaches have been presented, but there are still several essential problems that need to be solved. In this paper a novel fib ..."
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Cited by 43 (3 self)
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Abstract. The development of Diffusion Tensor MRI has raised hopes in the neuroscience community for in vivo methods to track fiber paths in the white matter. A number of approaches have been presented, but there are still several essential problems that need to be solved. In this paper a novel fiber propagation model is proposed, based on stochastics and regularization, allowing paths originating in one point to branch and return a probability distribution of possible paths. The proposed method utilizes the principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR). 1
Resampling Algorithms and Architectures for Distributed Particle Filters
 IEEE Transactions on Signal Processing
, 2004
"... In this paper, we propose novel resampling algorithms with architectures for e#cient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures a#ected by the resampling process. Problems in the particle filter implementation due to re ..."
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Cited by 40 (5 self)
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In this paper, we propose novel resampling algorithms with architectures for e#cient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures a#ected by the resampling process. Problems in the particle filter implementation due to resampling are described and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU) and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearingsonly tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for di#erent number of particles and di#erent level of parallelism with FPGA implementation. In this paper only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling.
Particle filters for mixture models with an unknown number of components
 Statistics and Computing
, 2003
"... We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in t ..."
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Cited by 38 (2 self)
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We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in the model, and to discretize the posterior distribution. Particle filters are particularly well suited to such discrete problems, and we propose the use of the particle filter of Fearnhead and Clifford for this problem. The performance of this particle filter, when analyzing both simulated and real data from a Gaussian mixture model, is uniformly better than the particle filter algorithm of Chen and Liu. In many situations it outperforms a Gibbs Sampler. We also show how models without the required amount of conjugacy can be efficiently analyzed by the same particle filter algorithm.
MultiRobot Cooperative Localization: A Study of Tradeoffs Between Efficiency and Accuracy
 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2002
"... This paper examines the tradeo#s between di#erent classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sampleb ..."
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Cited by 26 (2 self)
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This paper examines the tradeo#s between di#erent classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a samplebased (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots .