Using 2D Topological Map Information in a Markovian Image Segmentation

International audienceTopological map is a mathematical model of labeled image representation which contains both topological and geometrical information. In this work, we use this model to improve a Markovian seg-mentation algorithm. Image segmentation methods based on Markovian assumption consist in optimizing a Gibbs energy function. This energy function can be given by a sum of potentials which could be based on the shape or the size of a region, the number of adjacencies,.. . and can be computed by using topological map. In this work we propose the integration of a new potential: the global linearity of the boundaries, and show how this potential can be extracted from the topological map. Moreover, to decrease the complexity of our algorithm, we propose a local modification of the topological map in order to avoid the reconstruction of the entire structure

Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

International audienceRecent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner

Convolution mixture of FBF and modulated FBF and application to HRTEM images

International audienceIn this paper, we propose a mixture involving a fractional Brownian field and a modulated version of such a field for modeling High Resolution Transmission Electron Microscopy (HRTEM) textures. The mixture under consideration is defined from the convolution operator applied on spatial variables of the two fields under consideration. We present estimation methods for the parameters of the model (2 Hurst parameters and 2 spectral poles) based on Wavelet Packet (WP) spectrum. The relevance of our method is highlighted by its application to the analysis of HRTEM images with active phases of a catalyst.Dans cet article, nous proposons de modéliser certaines textures apparaissant dans les images à Haute Résolution de Microscopie Electronique en Transmission (HRMET) à l'aide d'un mélange composé d'un champ Brownien fractionnaire avec une version modulée d'un tel champ. Le mélange en question est basé sur un opérateur de convolution s'appliquant sur les variables spatiales des deux champs considérés. Nous présentons deux méthodes d'estimation des paramètres de Hurst du modèle en utilisant une approche par ondelettes. Nous présentons également une méthode pour localiser les pôles du modèle. Nous montrons la pertinence de ce modèle en l'appliquant à l'analyse des images HRMET contenant des phases actives d'un catalyseur. Abstract-In this paper, we propose a mixture involving a fractional Brownian field and a modulated version of such a field for modeling High Resolution Transmission Electron Microscopy (HRTEM) textures. The mixture under consideration is defined from the convolution operator applied on spatial variables of the two fields under consideration. We present estimation methods for the parameters of the model (2 Hurst parameters and 2 spectral poles) based on Wavelet Packet (WP) spectrum. The relevance of our method is highlighted by its application to the analysis of HRTEM images with active phases of a catalyst

ARFBF MODEL FOR NON STATIONARY RANDOM FIELDS AND APPLICATION IN HRTEM IMAGES

International audienceThis paper presents a new model called Autoregressive Fractional Brownian Field (ARFBF) for analyzing textures which contain stationary and non-stationary components. The paper also proposes two estimation methods for the parameter of an isotropic fractional Brownian field based on Wavelet Packet (WP) spectrum: the Log-Regression on Diagonal WP spectrum (Log-RDWP) and the Log-Regression on Polar representation of WP spectrum (Log-RPWP). The Log-RPWP method provides a better estimation performance for small size images. We show the interest of ARFBF model and Log-RPWP for characterizing High-Resolution Transmission Electron Microscopy (HRTEM) images

Nouvelle méthode de reconstruction de volumes de particules 3D basée sur les processus ponctuels marques : Application à la TOMO-PIV en mécanique de fluide

National audienceCet article représente un résumé des différents travaux effectués jusqu'à maintenant dans le cadre de ma thèse dans le département SIC du laboratoire XLIM et le département d2 (Fluide, Thermique, Combustion) du laboratoire P' de Poitiers sur les méthodes de reconstruction de volumes de particules 3d appliquées à la tomographie de vélocimétrie par image de particule (Tomo-PIV). Ces travaux s'inscrivent dans le cadre d'un projet européen intitulé AFDAR (Advanced Flow Diagnostics for Aeronautical Research project no. 265695). L'objectif de la thèse est de proposer une nouvelle technique de mesure tridimensionnelle de vitesse en mécanique des fluides par Tomo-PIV et de l'appliquer sur une étude expérimentale de la dissipation d'énergie turbulente derrière une grille

3D PARTICLE VOLUME TOMOGRAPHIC RECONSTRUCTION BASED ON MARKED POINT PROCESS: APPLICATION TO TOMO-PIV IN FLUID MECHANICS

International audienceIn recent years, marked point processes have received a great deal of attention. They were applied with success to extract objects in large data sets as those obtained in remote sensing frameworks or biological studies. We propose in this paper a method based on marked point processes to reconstruct volumes of 3D particles from images of 2D particles provided by the Tomographic Particle Image Velocimetry (Tomo-PIV) technique. Unlike other reconstruction methods, our approach allows us to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and naturally solves the memory problem which is inherent to pixel based approach used by classical tomographic reconstruction methods. The best reconstruction is found by minimizing an energy function which defines the marked point process. In order to avoid local minima, we use a simulated annealing algorithm. Results are presented on simulated data