4,806 research outputs found
A combination of particle filtering and deterministic approaches for multiple kernel tracking.
International audienceColor-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. This issue is overcome by using several kernels weighting pixels locations. In this paper a multiple kernels configuration is proposed and developed in both probabilistic and deterministic frameworks. The advantages of both approaches are combined to design a robust tracker allowing to track location, size and orientation of the object. A visual servoing application in tracking a moving object validates the proposed approach
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces
Nonlinear non-Gaussian state-space models arise in numerous applications in
statistics and signal processing. In this context, one of the most successful
and popular approximation techniques is the Sequential Monte Carlo (SMC)
algorithm, also known as particle filtering. Nevertheless, this method tends to
be inefficient when applied to high dimensional problems. In this paper, we
focus on another class of sequential inference methods, namely the Sequential
Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising
alternative to SMC methods. After providing a unifying framework for the class
of SMCMC approaches, we propose novel efficient strategies based on the
principle of Langevin diffusion and Hamiltonian dynamics in order to cope with
the increasing number of high-dimensional applications. Simulation results show
that the proposed algorithms achieve significantly better performance compared
to existing algorithms
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure
Parallel resampling in the particle filter
Modern parallel computing devices, such as the graphics processing unit
(GPU), have gained significant traction in scientific and statistical
computing. They are particularly well-suited to data-parallel algorithms such
as the particle filter, or more generally Sequential Monte Carlo (SMC), which
are increasingly used in statistical inference. SMC methods carry a set of
weighted particles through repeated propagation, weighting and resampling
steps. The propagation and weighting steps are straightforward to parallelise,
as they require only independent operations on each particle. The resampling
step is more difficult, as standard schemes require a collective operation,
such as a sum, across particle weights. Focusing on this resampling step, we
analyse two alternative schemes that do not involve a collective operation
(Metropolis and rejection resamplers), and compare them to standard schemes
(multinomial, stratified and systematic resamplers). We find that, in certain
circumstances, the alternative resamplers can perform significantly faster on a
GPU, and to a lesser extent on a CPU, than the standard approaches. Moreover,
in single precision, the standard approaches are numerically biased for upwards
of hundreds of thousands of particles, while the alternatives are not. This is
particularly important given greater single- than double-precision throughput
on modern devices, and the consequent temptation to use single precision with a
greater number of particles. Finally, we provide auxiliary functions useful for
implementation, such as for the permutation of ancestry vectors to enable
in-place propagation.Comment: 21 pages, 6 figure
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