Results 11  20
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59
Automatic extraction of femur contours from calibrated xray images: A Bayesian inference approach
 In: Proc Biomed Imag
, 2008
"... Abstract — Automatic identification and extraction of bone contours from xray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated xray images. The automa ..."
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Abstract — Automatic identification and extraction of bone contours from xray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated xray images. The automatic initialization to align the 3D model with the xray images is solved by an Estimation of Bayesian Network Algorithm to fit a simplified multiple component geometrical model of the proximal femur to the xray data. Landmarks can be extracted from the geometrical model for the initialization of the 3D statistical model. The contour extraction is then accomplished by a joint registration and segmentation procedure. We iteratively updates the extracted bone contours and an instanced 3D model to fit the xray images. Taking the projected silhouettes of the instanced 3D model on the registered xray images as templates, bone contours can be extracted by a graphical model based Bayesian inference. The 3D model can then be updated by a nonrigid 2D/3D registration between the 3D statistical model and the extracted bone contours. Preliminary experiments on clinical data sets verified its validity. Index Terms — contour extraction, statistical model, Bayesian network, 2D/3D registration, segmentation, calibrated xray image I.
A BackwardSimulation Based RaoBlackwellized Particle Smoother for Conditionally Linear Gaussian Models
"... Abstract: In this article, we develop a new RaoBlackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forwardfiltering backwardsimulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space ..."
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Abstract: In this article, we develop a new RaoBlackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forwardfiltering backwardsimulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the nonGaussian state component while treating the linear part analytically. Unlike the previously proposed backwardsimulation based RaoBlackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of twofilter smoother based approaches. The performance of the algorithm is illustrated in a simulated application.
2012, Sequential Bayesian techniques applied to nonvolcanic tremor
 Journal of Geophysical Research
"... [1] This paper uses sequential Bayesian techniques such as particle filters and smoothers to track in time both the nonvolcanic tremor (NVT) source location on the plate interface and the angle of arrival via horizontal phase slowness. Sequential Bayesian techniques enable tracking of evolving geop ..."
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Cited by 4 (1 self)
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[1] This paper uses sequential Bayesian techniques such as particle filters and smoothers to track in time both the nonvolcanic tremor (NVT) source location on the plate interface and the angle of arrival via horizontal phase slowness. Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential tremor observations. These techniques provide a formulation where the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. In addition to the optimal solution, particle filters and smoothers can calculate the underlying probability densities for the desired parameters, providing the uncertainties in the estimates. The tremor tracking has been performed using array beamforming. Here it is demonstrated that the uncertainties both in the NVT source location estimates and phase slowness estimates are reduced using a particle filter compared to just using a beamformer based inversion. Particle smoothers further reduces the uncertainty, giving the best performance out of the three methods used here.
Unscented Kalman filter/smoother for a CBRN puffbased dispersion model
 in Proc. Int. Conf. Inf. Fusion
, 2007
"... Abstract—Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A twofilter smoother that uses a Backward SigmaPoint Information Filter ..."
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Abstract—Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A twofilter smoother that uses a Backward SigmaPoint Information Filter, and also a forwardbackward RauchTungStriebel (RTS) smoothing form are rederived using the weighted statistical linearization concept. Both methods are derived in the context of the Unscented Kalman Filter. The square root version of the resulting RTS Unscented Kalman Filter / Smoother is applied to a CBRN dispersion puffbased model with variable state dimension, and the data assimilation performance of the method is compared with a Particle Filter implementation.
Approximate Bayesian computation for smoothing
 Stoch. Anal. Appl
, 2014
"... We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of au ..."
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We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter > 0. We provide theoretical results which quantify, in terms of , the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is O(n), where n is the number of time steps over which smoothing is performed. For numerical implementation we adopt the forwardonly sequential Monte Carlo (SMC) scheme of [16] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. When the HMM has unknown static parameters, we consider particle Markov chain Monte Carlo [2] (PMCMC) methods for batch statistical inference.
Sequential quasiMonte Carlo
, 2014
"... We develop a new class of algorithms, SQMC (Sequential QuasiMonte Carlo), as a variant of SMC (Sequential Monte Carlo) based on lowdiscrepancy point sets. The complexity of SQMC is O(N logN), where N is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo ..."
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Cited by 3 (2 self)
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We develop a new class of algorithms, SQMC (Sequential QuasiMonte Carlo), as a variant of SMC (Sequential Monte Carlo) based on lowdiscrepancy point sets. The complexity of SQMC is O(N logN), where N is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo rate O(N−1/2). The only requirement to implement SQMC is the ability to write the simulation of particle xnt given xnt−1 as a deterministic function of xnt−1 and a fixed number of uniform variates. We show that SQMC is amenable to the same extensions as standard SMC, such as forward smoothing, backward smoothing, unbiased likelihood evaluation, and so on. In particular, SQMC may replace SMC within a PMCMC (particle Markov chain Monte Carlo) algorithm. We establish several convergence results. These convergence results also apply, as a corollary, to the arrayRQMC algorithm of L’Ecuyer et al. (2006). We provide numerical evidence that SQMC may significantly outperform SMC in terms of approximation error in practical scenarios.
Identification of Mixed Linear/Nonlinear StateSpace Models
"... Abstract — The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear statespace models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expec ..."
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Abstract — The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear statespace models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using a so called RaoBlackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study. I.
ADAPTIVE STOPPING FOR FAST PARTICLE SMOOTHING
"... Particle smoothing is useful for offline state inference and parameter learning in nonlinear/nonGaussian statespace models. However, many particle smoothers, such as the popular forward filter/backward simulator (FFBS), are plagued by a quadratic computational complexity in the number of particles ..."
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Particle smoothing is useful for offline state inference and parameter learning in nonlinear/nonGaussian statespace models. However, many particle smoothers, such as the popular forward filter/backward simulator (FFBS), are plagued by a quadratic computational complexity in the number of particles. One approach to tackle this issue is to use rejectionsamplingbased FFBS (RSFFBS), which asymptotically reaches linear complexity. In practice, however, the constants can be quite large and the actual gain in computational time limited. In this contribution, we develop a hybrid method, governed by an adaptive stopping rule, in order to exploit the benefits, but avoid the drawbacks, of RSFFBS. The resulting particle smoother is shown in a simulation study to be considerably more computationally efficient than both FFBS and RSFFBS. Index Terms — Sequential Monte Carlo, particle smoothing, backward simulation. 1.
A NEW APPROACH TO PARTICLE BASED SMOOTHED MARGINAL MAP
"... We present here a new method of finding the MAP state estimator from the weighted particles representation of marginal smoother distribution. This is in contrast to the usual practice, where the particle with the highest weight is selected as the MAP, although the latter is not necessarily the most ..."
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We present here a new method of finding the MAP state estimator from the weighted particles representation of marginal smoother distribution. This is in contrast to the usual practice, where the particle with the highest weight is selected as the MAP, although the latter is not necessarily the most probable state estimate. The method developed here uses only particles with corresponding filtering and smoothing weights. We apply this estimator for finding the unknown initial state of a dynamical system and addressing the parameter estimation problem. 1.