23 research outputs found
Lattice Green functions and diffusion for modelling traffic routing in ad hoc networks
We describe basic properties of Markov chains on finite state spaces and their application to Green functions, partial differential equations, and their (approximate) solution using random walks on a graph. Attention is paid to the influence of boundary conditions (Dirichlet/von Neumann). We apply these ideas to the study of traffic propagation and distribution in ad hoc networks
Potts models and image labelling relaxation by random Markov fields
We show in this paper the deep relationship between classic models from
Statistical Physics and Markovian Random Fields models used in image
labelling. We present as an application a markovian relaxation method
for enhancement and relaxation of previously classified images . An
energy function is defined, which depends only on the labels and on their
initial value . The main a priori pixel knowledge results from the confusion
matrix of the reference samples used for initial classification . The energy
to be minimized includes also terms ensuring simultaneous spatial label
regularty, growth of some classes and disparition of some others. The
method allows for example to reclassify previous rejection class pixels in their spatial environment . Last we present some results on Remote
Sensing multispectral and geological ore images, comparing the performances
of Iterated Conditional Modes (ICM) and Simulated Annealing
(SA) . Very low CPU time was obtained due to the principle of the method,
working on labels instead of gray levels .Nous montrons dans cet article la relation profonde entre certains modèles d'énergie provenant de la Physique Statistique utilisés et les modèles utilisés en champ de Markov pour l'étiquetage d'images. Nous présentons comme application une méthode markovienne de relaxation et d'amélioration d'images préclassifiées. On définit pour cela une fonction énergie ne dépendant que des labels et de leur valeur initiale, la connaissance a priori sur l'image provenant de la matrice de confusion déduite des échantillons de référence utilisés pour la classification initiale. La fonction à minimiser inclut divers termes assurant la régularité spatiale des labels, la croissance ou la disparition de certaines classe
A spatial regularization method preserving local photometry for Richardson-Lucy restoration
In this article we give a simple method of spatial regularization
deriving from the Richardson-Lucy (RL) algorithm in order to overcome the problem
of noise amplification during the image reconstruction process.
It is very important in astronomy
to regularize images while controlling their photometric behavior.
We propose a new reconstruction method preserving
both the global and local image photometric properties.
A mathematical presentation
is described here. This method was applied to
an image of Titan -a satellite of Saturn -acquired in near infrared
with the adaptive optics system ADONIS installed at the ESO 3.6 m telescope
in La Silla (Chile). The local photometric results here are compared using
contours normalized at the same intensity levels
Solar image segmentation by use of mean field fast annealing
We present a "continuous” analysis of a solar image
in order to address the problem of image segmentation. Our approach
is based on combinatorial optimization methods and in particular on
Mean Field Fast Annealing (MFFA).
Mean-field theory gives a deterministic nature to our algorithm
while its efficiency is improved by a fast cooling schedule.
We show how this method can be used to separate efficiently the
regions of different solar activity giving a tool for a future
automated recognition and classification of sunspots
Solar image segmentation by use of mean field fast annealing
We present a “continuous” analysis of a solar H-alpha image in order
to address the problem of image segmentation. Our approach is based on
combinatorial optimization methods and in particular on Mean Field Fast
Annealing (MFFA). Mean-field theory gives a deterministic nature to our
algorithm while its efficiency is improved by a fast cooling schedule.
We show how this method call be used to separate efficiently the regions
of different solar activity giving a tool for a future automated
recognition and classification of sunspots
Image relaxation by use of the Potts model with a fast deterministic method
We demonstrate a close relationship between classical models from statistical physics and Markov randomfield models for image labeling purposes. A picture is taken of a real image or map, defined by a measure of intensity over a set of pixels. Possible class (or gray-level) values are assigned to spin values in Potts theory. We present a continuous analysis of image relaxation by mean-field theory and apply it by using standard and extended Potts models. Accurate relaxation results were obtained with a specific deterministic method called mean-field fast annealing. © 1997 Optical Society of America
Application de la renormalisation a l'analyse de textures markoviennes gaussiennes
Le problème de l'estimation des paramètres associés aux champs markoviens est souvent abordé soit conjointement à un problème de restauration, ou de segmentation, soit directement dans le cadre d'approximations à partir du pseudo-maximum de vraisemblance. Ces méthodes se révèlent inappropriées dans le cadre de l'analyse de textures où une discrimination fine nécessite une estimation précise des paramètres caractéristiques du modèle markovien. Dans cet article, nous proposons une méthode pour l'estimation exacte des paramètres d'un modèle markovien gaussien 4-connexe reposant sur la théorie de la renormalisation. Tout d'abord, nous démontrons de façon analytique le caractère gaussien de la texture décimée, puis nous donnons les estimateurs conjoints, associés aux textures initiale et décimée permettant alors d'estimer les paramètres caractéristiques de la texture originale. Précision et robustesse des estimations sont établies à partir d'images de synthèse de textures stationnaires ou non
Building Block Extraction and Classification by means of Markov Random Fields using Aerial Imagery and LiDAR Data
Building detection has been a prominent area in the area of image
classification. Most of the research effort is adapted to the specific
application requirements and available datasets. Our dataset includes
aerial orthophotos (with spatial resolution 20cm), a DSM generated from
LiDAR (with spatial resolution 1m and elevation resolution 20 cm) and
DTM (spatial resolution 2m) from an area of Athens, Greece. Our aim is
to classify these data by means of Markov Random Fields (MRFs) in a
Bayesian framework for building block extraction and perform a
comparative analysis with other supervised classification techniques
namely Feed Forward Neural Net (FFNN), Cascade-Correlation Neural
Network (CCNN), Learning Vector Quantization (LVQ) and Support Vector
Machines (SVM). We evaluated the performance of each method using a
subset of the test area. We present the classified images, and
statistical measures (confusion matrix, kappa coefficient and overall
accuracy). Our results demonstrate that the MRFs and FFNN perform better
than the other methods
Markov Random Fields based probabilistic relaying for multihop networks
In this paper, we demonstrate how a Markov Random Field (MRF) based framework can be used for multihop networks analysis and design. In fields such as image processing it has been shown that MRFs is a powerful tool to analyse distributed systems with strong spacial interactions, which is also a defining characteristic of multihop networks. In this work we focus on using MRFs to model traffic intensity of sensor networks using shortest path routing. Later we propose a probabilistic relaying mechanism to recreate a traffic pattern similar to that observed in a network using shortest path routing. The objective is to emulate the shortest path performance without complex routing protocols and associated overheads. Using a simulation study we then show that the proposed mechanism achieves 95% of the throughput of shortest path, without using a routing protocol