Search results for "Random field"

Système de prise d'images haute résolution pour l'analyse de projection de particules : application à l'épandage centrifuge d'engrais

2002

This paper describes the design of a high resolution low cost imaging system for the analysis of high speed particle projection. This system, based on a camera and a set of flashes, is used to characterize the centrifugal spreading of fertilizer particles ejected at speeds of environ 30 m s. Multiexposure images collected with the camera installed perpendicular to the output flow of granules are analysed to estimate the trajectories of the fertilizer granules. Very good results are obtained with the Markov random fields method, in comparison with others.

Échantillonnage adaptatif optimal dans les champs de Markov, application à l’échantillonnage d’une espèce adventice

2012

This work is divided into two parts: (i) the theoretical study of the problem of adaptive sampling in Markov Random Fields (MRF) and (ii) the modeling of the problem of weed sampling in a crop field and the design of adaptive sampling strategies for this problem. For the first point, we first modeled the problem of finding an optimal sampling strategy as a finite horizon Markov Decision Process (MDP). Then, we proposed a generic algorithm for computing an approximate solution to any finite horizon MDP with known model. This algorithm, called Least-Squared Dynamic Programming (LSDP), combines the concepts of dynamic programming and reinforcement learning. It was then adapted to compute adapt…

Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

2016

Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…

Random-strain-field effects on the low-temperature state of KCN

1991

Random strain fields have been introduced into samples of KCN by pressing pellets from powder and by thermal cycling. X-ray diffractograms show that the low-temperature structure depends strongly on the sample history. In some cycles the noncubic low-temperature phases have been suppressed in mesoscopic surface regions of the samples and the quadrupolar-glass state formed instead.

Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…

Modeling and Mitigating Errors in Belief Propagation for Distributed Detection

2021

We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distribute…

Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes

2018

We propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatio-temporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast procedure, based on the spatio-temporal pair correlation function, provides reliable estimates and we compare the results with the current available methods. Moreover, the proposed method can be used in the case of both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field. We describe earthquake sequences comparing several Cox model specifications.

On the asymptotic behaviour of gaussian spherical integrals

1983