Search results for " random field"

Using Fourier local magnitude in adaptive smoothness constraints in motion estimation

2007

Like many problems in image analysis, motion estimation is an ill-posed one, since the available data do not always sufficiently constrain the solution. It is therefore necessary to regularize the solution by imposing a smoothness constraint. One of the main difficulties while estimating motion is to preserve the discontinuities of the motion field. In this paper, we address this problem by integrating the motion magnitude information obtained by the Fourier analysis into the smoothness constraint, resulting in an adaptive smoothness. We describe how to achieve this with two different motion estimation approaches: the Horn and Schunck method and the Markov Random Field (MRF) modeling. The t…

On the use of adaptive spatial weight matrices from disease mapping multivariate analyses

2020

Conditional autoregressive distributions are commonly used to model spatial dependence between nearby geographic units in disease mapping studies. These distributions induce spatial dependence by means of a spatial weights matrix that quantifies the strength of dependence between any two neighboring spatial units. The most common procedure for defining that spatial weights matrix is using an adjacency criterion. In that case, all pairs of spatial units with adjacent borders are given the same weight (typically 1) and the remaining non-adjacent units are assigned a weight of 0. However, assuming all spatial neighbors in a model to be equally influential could be possibly a too rigid or inapp…

Unified Analysis of Periodization-Based Sampling Methods for Matérn Covariances

2020

The periodization of a stationary Gaussian random field on a sufficiently large torus comprising the spatial domain of interest is the basis of various efficient computational methods, such as the ...

Polynomial approximation of non-Gaussian unitaries by counting one photon at a time

2017

In quantum computation with continous-variable systems, quantum advantage can only be achieved if some non-Gaussian resource is available. Yet, non-Gaussian unitary evolutions and measurements suited for computation are challenging to realize in the lab. We propose and analyze two methods to apply a polynomial approximation of any unitary operator diagonal in the amplitude quadrature representation, including non-Gaussian operators, to an unknown input state. Our protocols use as a primary non-Gaussian resource a single-photon counter. We use the fidelity of the transformation with the target one on Fock and coherent states to assess the quality of the approximate gate.

KINETICS OF CRYSTAL GROWTH LIMITED BY RANDOM VELOCITY FIELDS

2008

A spherical growth process controlled by velocity fluctuations of particles of a saturated solution is investigated. Velocity fluctuations are modeled by a Gaussian random field. The interface evolution is determined by a Langevin-type equation with a multiplicative random field, which in the case of the quasi-homogeneous random Gaussian field is equivalent to Fokker–Planck dynamics. We analyze numerically the Fokker–Planck equation and compare growth kinetics in the case of noisy (i.e. space-independent) fluctuations. It is shown that for a large class of spatially correlated velocity fluctuations, the growth kinetics is universal, i.e. it does not depend on the details of statistics of f…

[IC‐P‐029]: GAUSSIAN MARKOV RANDOM FIELDS FOR ASSESSING INTERMODAL REGIONAL ASSOCIATIONS IN PRODROMAL ALZHEIMER's DISEASE

2017

Non-Markovianity of Gaussian Channels

2015

We introduce a necessary and sufficient criterion for the non-Markovianity of Gaussian quantum dynamical maps based on the violation of divisibility. The criterion is derived by defining a general vectorial representation of the covariance matrix which is then exploited to determine the condition for the complete positivity of partial maps associated to arbitrary time intervals. Such construction does not rely on the Choi-Jamiolkowski representation and does not require optimization over states.

CORRELATIONS AMONG FORWARD RETURNS IN THE NORDIC ELECTRICITY MARKET

2009

I analyze empirical correlations of electricity forward returns from the perspective of a random field model that specifies the correlations in terms of the temporal separation between forward maturities. It turns out that temporal separation cannot fully account for the empirical forward return correlations. Specifically, the relation between correlations and temporal separation does not seem to be invariant across segments of the electricity forward market or trading periods.

Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

2016

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…

A mutual GrabCut method to solve co-segmentation

2013

Publised version of an article from the journal:Eurasip Journal on Image and Video Processing. Also available on SpringerLink:http://dx.doi.org/10.1186/1687-5281-2013-20. Open Access Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model ra…