5,214 research outputs found
Knowledge-based segmentation of SAR data with learned priors
©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.821747An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data
Exact Simulation of Wishart Multidimensional Stochastic Volatility Model
In this article, we propose an exact simulation method of the Wishart
multidimensional stochastic volatility (WMSV) model, which was recently
introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of
the conditional characteristic function of the log-price given volatility
level. In particular, we found an explicit expression for the conditional
characteristic function for the Heston model. We perform numerical experiments
to demonstrate the performance and accuracy of our method. As a result of
numerical experiments, it is shown that our new method is much faster and
reliable than Euler discretization method.Comment: 27 page
Computing transition rates for the 1-D stochastic Ginzburg--Landau--Allen--Cahn equation for finite-amplitude noise with a rare event algorithm
In this paper we compute and analyse the transition rates and duration of
reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the
Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude
white noise, as well as for small and large domain. We demonstrate that
extremely rare reactive trajectories corresponding to direct transitions
between two metastable states are efficiently computed using an algorithm
called adaptive multilevel splitting. This algorithm is dedicated to the
computation of rare events and is able to provide ensembles of reactive
trajectories in a very efficient way. In the small noise limit, our numerical
results are in agreement with large-deviation predictions such as
instanton-like solutions, mean first passages and escape probabilities. We show
that the duration of reactive trajectories follows a Gumbel distribution like
for one degree of freedom systems. Moreover, the mean duration growths
logarithmically with the inverse temperature. The prefactor given by the
potential curvature grows exponentially with size. The main novelty of our work
is that we also perform an analysis of reactive trajectories for large noises
and large domains. In this case, we show that the position of the reactive
front is essentially a random walk. This time, the mean duration grows linearly
with the inverse temperature and quadratically with the size. Using a
phenomenological description of the system, we are able to calculate the
transition rate, although the dynamics is described by neither
Freidlin--Wentzell or Eyring--Kramers type of results. Numerical results
confirm our analysis
Adaptive MCMC Methods for Inference on Discretely Observed Affine Jump Diffusion Models.
In the present paper we generalize in a Bayesian framework the inferential solution proposed by Eraker, Johannes & Polson (2003) for stochastic volatility models with jumps and affine structure. We will use an adaptive sampling methodology known as Delayed Rejection suggested in Tierney & Mira (1999) in a Markov Chain Monte Carlo settings in order to reduce the asymptotic variance of the estimates. Furthermore, the use of a particle filtering procedure allows to compute the Bayes factor
Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions
In this paper, we propose a novel large deformation diffeomorphic
registration algorithm to align high angular resolution diffusion images
(HARDI) characterized by orientation distribution functions (ODFs). Our
proposed algorithm seeks an optimal diffeomorphism of large deformation between
two ODF fields in a spatial volume domain and at the same time, locally
reorients an ODF in a manner such that it remains consistent with the
surrounding anatomical structure. To this end, we first review the Riemannian
manifold of ODFs. We then define the reorientation of an ODF when an affine
transformation is applied and subsequently, define the diffeomorphic group
action to be applied on the ODF based on this reorientation. We incorporate the
Riemannian metric of ODFs for quantifying the similarity of two HARDI images
into a variational problem defined under the large deformation diffeomorphic
metric mapping (LDDMM) framework. We finally derive the gradient of the cost
function in both Riemannian spaces of diffeomorphisms and the ODFs, and present
its numerical implementation. Both synthetic and real brain HARDI data are used
to illustrate the performance of our registration algorithm
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