DESCOMBES , Xavier

Probabilistic approaches have been brought to image analysis starting with the famous paper by Besag [1], inspired by the Gibbs field theory developped in statistical physics. Later on, this approach mostly developped in the Bayesian framework has become very popular in the image processing community from the texture modeling proposed by Cross...

The marked point process framework has been successfully developed in the field of image analysis to detect a configuration of predefined objects. The goal of this paper is to show how it can be particularly applied to biological imagery. We present a simple model that shows how some of the challenges specific to biological data are well...

This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous...

Probabilistic approaches have been brought to image analysis starting with the famous paper by Besag [1], inspired by the Gibbs field theory developped in statistical physics. Later on, this approach mostly developped in the Bayesian framework has become very popular in the image processing community from the texture modeling proposed by Cross...

Ce mémoire résume mes travaux sur la décennie (entre 1994 et 2003) suivant ma thèse de doctorat qui était consacrée aux champs de Markov en analyse d'image et fut soutenue en décembre 1993. Ces travaux ont vu le jour dans différents laboratoires d'accueil : – Laboratoire Image - Télécom Paris – Université Catholique de Louvain (K.U.Leuven) –...

In this chapter, we consider a marked point process framework for analyzing high resolution images, which can be interpreted as an extension of the Markov random field modelling (see Chaps. 14 and 15). The targeted applications concern object detection. Similarly to Chap. 10, we assume that the information embedded in the image consists of a...

Gibbs Fields are widely used in image processing for both segmentation and restoration. Defined on a discrete lattice representing the image they exhibit a non-isotropic behavior. Herein, we study and quantify this non-isotropy by computing the boundary tension as a function of the angle of a line separating the plane in two parts containing a...

We address the problem of image denoising using a Stochastic Differential Equation approach. We consider a diffusion process which converges to a Gibbs measure. The Hamiltonian of the Gibbs measure embeds an interaction term, providing smoothing properties, and a data term. We study two discrete approximations of the Langevin dynamics...

We consider relaxation properties of new multiple birth and death (MBAD) stochastic algorithms developed recently for problems of detection and feature extraction in image analysis. The MBAD algorithms are based on birth-and-death stochastic dynamics in continuum and its discrete time ap- proximation. We found the relation between cooling...

This paper is a review of some probabilistic methods, based on Gibbs fields theory, applied to solve image analysis tasks. We present the mathematical background and show different applications.

Dans cette étude, nous comparons l'efficacité de deux techniques de simulation par chaînes de Markov (MCMC) de processus aléatoires sur des ensembles d'objets géométriques : l'algorithme de naissance-mort et celui de Metropolis--Hastings-Green. Les comparaisons sont effectuées sur différents modèles de processus objets de type attractif...

We present a new model for extracting tree crowns from aerial images, containing a near infrared channel. We addres s the problem as an image segmentation problem solved by a Markov Random Field (MRF) modeling embedded in a Bayesian framework. The prior model consists of a couple field defined by a three state MRF, associated to vegetation,...

Les nouvelles données images très haute résolution ont beaucoup apporté aux inventaires forestiers en fournissant des informations à l'échelle de l'arbre. Des algorithmes, chargés de récupérer ces informations automatiquement, sont nécessaires pour aider les photo-interprètes.

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and...

We propose some improvements of the Multiple Birth and Cut algorithm (MBC) in order to extract nuclei in 2D and 3D images. This algorithm based on marked point processes was proposed recently in (Gamal Eldin et al., 2012). We introduce a new contrast invariant energy that is robust to degradations encountered in fluorescence microscopy (e.g....

Significance: Reflectance confocal microscopy (RCM) allows for real-time in vivo visualization of the skin at the cellular level. The study of RCM images provides information on the structural properties of the epidermis. These may change in each layer of the epidermis, depending on the subject's age and the presence of certain dermatological...

International audience

We present a textural kernel for "Support Vector Machines" classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We introduce kernel functions in order to favor the distiction between our class of interest and the other classes :...

In this report we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The...

The morphological analysis of axonal trees is an important problem in neuroscience. The first step for such an analysis is the extraction of the axon. Due to the high volume of generated image data and the tortuous nature of the axons, manual processing is not feasible. Therefore, it is necessary to develop techniques for the automatic...

Nous cherchons à extraire des houppiers à partir d'images de télédétection. Pour ce faire, nous construisons un processus objet et assimilons nos images d'arbres à des réalisations de ce processus. La première étape consiste à définir d'une part les objets géométriques modélisant les arbres, et d'autre part la densité du processus à simuler.La...

This work present an automatic algorithm that extract 3D land register from altimetric data in dense urban areas. Altimetry of a town is a data which is easily available yet difficult to exploit. For instance, we present here results on two kind of measurements : the first one consists in a Digital Elevation Model (DEM) built using a...

High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable forestry managers to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the resources. Automatic algorithms are needed in that prospect to assist human operators in the...

This report presents a new stochastic geometry model for unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates strong geometrical and topological constraints through potentials on the...