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Object Tracking with an Adaptive ColorBased Particle Filter
, 2002
"... Color can provide an efficient visual feature for tracking nonrigid objects in realtime. However, the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters. To handle these appearance changes a colorbased target model must be adapted du ..."
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Cited by 20 (0 self)
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Color can provide an efficient visual feature for tracking nonrigid objects in realtime. However, the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters. To handle these appearance changes a colorbased target model must be adapted during temporally stable image observations.
Approximation and Limit Results for Nonlinear Filters over an Infinite Time Interval: Part II, Random Sampling Algorithms
"... The paper is concerned with approximations to nonlinear filtering problems that are of interest over a very long time interval. Since the optimal filter can rarely be constructed, one needs to compute with numerically feasible approximations. The signal model can be a jumpdiffusion, reflected or no ..."
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Cited by 19 (8 self)
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The paper is concerned with approximations to nonlinear filtering problems that are of interest over a very long time interval. Since the optimal filter can rarely be constructed, one needs to compute with numerically feasible approximations. The signal model can be a jumpdiffusion, reflected or not. The observations can be taken either in discrete or continuous time. The cost of interest is the pathwise error per unit time over a long time interval. In a previous paper of the authors [2], it was shown, under quite reasonable conditions on the approximating filter and on the signal and noise processes that (as time, bandwidth, process and filter approximation, etc.) go to their limit in any way at all, the limit of the pathwise average costs per unit time is just what one would get if the approximating processes were replaced by their ideal values and the optimal filter were used. When suitable approximating filters cannot be readily constructed due to excessive computational requirem...
Population Monte Carlo algorithms
 In Transactions of the Japanese Society for Artificial Intelligence
, 2001
"... We give a crossdisciplinary survey on “population ” Monte Carlo algorithms. In these algorithms, a set of “walkers ” or “particles ” is used as a representation of a highdimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are deve ..."
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Cited by 11 (1 self)
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We give a crossdisciplinary survey on “population ” Monte Carlo algorithms. In these algorithms, a set of “walkers ” or “particles ” is used as a representation of a highdimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms – “quantum Monte Carlo”, “transfermatrix Monte Carlo”, “Monte Carlo filter (particle filter)”,“sequential Monte Carlo ” and “PERM ” etc. Here we discuss them in a coherent framework. We also touch on related algorithms – genetic algorithms and annealed importance sampling.
Population Monte Carlo algorithms Trans
 Jpn. Soc. Artif. Intell
, 2001
"... Abstract: In this paper, we give a crossdisciplinary survey on “populationbased ” Monte Carlo algorithms. These algorithms consist of a set of “walkers ” or “particles ” for the representation of a highdimensional vector and the computation is carried out by a random walk and split/deletion of th ..."
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Cited by 10 (0 self)
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Abstract: In this paper, we give a crossdisciplinary survey on “populationbased ” Monte Carlo algorithms. These algorithms consist of a set of “walkers ” or “particles ” for the representation of a highdimensional vector and the computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms – “Quantum Monte Carlo”, “Transfer Matrix Monte Carlo”, “Monte Carlo Filter (Particle Filter)”,“Sequential Monte Carlo ” and “PERM ” etc. Here we discuss them in a coherent framework. We also
Advances towards an implantable motor cortical interface
 University of Utah, Salt Lake City
, 2001
"... A number of neurological disorders may lead to varying degrees of paralysis in humans. A BrainComputer Interface (BCI) based on an array of microelectrodes chronically implanted in the primary motor cortex can be used as an augmentative communication and control device for the paralyzed, essentiall ..."
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Cited by 9 (2 self)
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A number of neurological disorders may lead to varying degrees of paralysis in humans. A BrainComputer Interface (BCI) based on an array of microelectrodes chronically implanted in the primary motor cortex can be used as an augmentative communication and control device for the paralyzed, essentially bypassing the damaged motor pathway by directly accessing and translating volitional control signals from populations of neurons in the patient's brain. In this research, the constraints imposed by motor cortical physiology on an implantable BCI are studied, and novel automatic signal processing strategies for extracting information from the noisy neurophysiological signals are developed. Three specific issues were investigated. First, the ability of paralyzed individuals to internally modulate central activity patterns when attempting to move paralyzed limbs is established using functional Magnetic Resonance Imaging (fMRI), a noninvasive imaging method. Activity maps that qualitatively and quantitatively resemble the normal homuncular representation of limbs are demonstrated in the primary sensorimotor cortex as well as in other motor areas
Color Features for Tracking NonRigid Objects
 Special Issue on Visual Surveillance, Chinese Journal of Automation, May 2003
, 2003
"... Robust realtime tracking of nonrigid objects is a challenging task. Color distributions provide an efficient feature for this kind of tracking problems as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. This article presents the integration of ..."
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Cited by 7 (0 self)
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Robust realtime tracking of nonrigid objects is a challenging task. Color distributions provide an efficient feature for this kind of tracking problems as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. This article presents the integration of color distributions into particle filtering, which has typically been used in combination with edgebased image features. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in cases of clutter and occlusion. To overcome the problem of appearance changes, an adaptive model update is introduced during temporally stable image observations. Furthermore, an initialization strategy is discussed since tracked objects may disappear and reappear. Keywords: particle filtering, color distribution, Bhattacharyya coefficient.
Bayesian Multiple Target Tracking in Forward Scan Sonar Images Using the PHD
 Filter,” IEE Radar, Sonar and Navigation
, 2005
"... A multiple target tracking algorithm for forwardlooking sonar images is presented. The algorithm will track a variable number of targets estimating both the number of targets and their locations. Targets are tracked from range and bearing measurements by estimating the firstorder statistical momen ..."
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Cited by 7 (3 self)
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A multiple target tracking algorithm for forwardlooking sonar images is presented. The algorithm will track a variable number of targets estimating both the number of targets and their locations. Targets are tracked from range and bearing measurements by estimating the firstorder statistical moment of the multitarget probability distribution called the Probability Hypothesis Density (PHD). The recursive estimation of the PHD is much less computationally expensive than estimating the joint multitarget probability distribution. Results are presented showing a variable number of targets being tracked with targets entering and leaving the Field of View. An initial implementation is shown to work on a simulated sonar trajectory and an example is shown working on real data with clutter. I.
Monte Carlo algorithms and asymptotic problems in nonlinear filtering
 To Appear in Stochastics in Finite/Infinite Dimensions (Volume in honor of Gopinath Kallianpur
, 1999
"... This paper is an extension of [4], which dealt with a wide variety of approximations to optimal nonlinear filters over long time intervals, where ..."
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Cited by 6 (2 self)
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This paper is an extension of [4], which dealt with a wide variety of approximations to optimal nonlinear filters over long time intervals, where
Monte Carlo Methods in State Space Estimation
, 1996
"... Monte Carlo methods are  generally speaking  techniques for doing numerical integration. They are applicable to a wide range of problems, mainly because Monte Carlo methods are general. They can be used to solve problems where an analytical approach is impossible. Even where analytical technique ..."
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Cited by 3 (0 self)
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Monte Carlo methods are  generally speaking  techniques for doing numerical integration. They are applicable to a wide range of problems, mainly because Monte Carlo methods are general. They can be used to solve problems where an analytical approach is impossible. Even where analytical techniques can be used, a Monte Carlo approach may be simpler and less time consuming. Monte Carlo methods tend to be machine intensive, but as the speed of computers continuously increase this is hardly a problem. State space models are used in numerous practical applications. Tracking, guidance and navigation are just a few of the areas where state space estimation plays an important role. Monte Carlo methods have in recent year appeared in the field of state space estimation. At first, Monte Carlo techniques were used to perform smoothing in state space models. The first method for doing Monte Carlo recursive filtering was introduced in 1991 and the first useful method was introduced in 1993. ...
Online filtering for nonlinear/ nonGaussian state space models
, 1996
"... :The bootstrap filter is an algorithm for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise. In sit ..."
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Cited by 1 (0 self)
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:The bootstrap filter is an algorithm for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise. In situations where there is low overlap between prior and posterior, the standard bootstrap filter may not work well since much probability mass will be concentrated on few samples. The hybrid bootstrap approach attempts to alleviate this by interpolating continuous mass in between discrete samples. We apply both filtering methods to some econometric data to show that the hybrid approach can lead to improved estimation with smaller sample sets. Keywords:Recursive filtering, Bootstrap filter, nonstationary time series, mixture distribution, nonGaussian filter. 1. Introduction In practically every branch of science one encounters problems where the requirement is to estimate the state of a syst...