This thesis looks at the evaluation of textures in two different perspectives using logarithmic image processing (LIP) framework. The first case after introducing the concept of textures and giving some classical approaches of textures evaluation, it gives an original approach of textures evaluation called covariogram which is derived from similarity metrics like distances or correlations etc. The classical covariogram which is derived from the classical similarity metrics and LIP covariogram are then applied over several images and the efficiency of the LIP one is clearly shown for darkened images. The last two chapters offer a new approach by considering the gray levels of an image as the phases of a medium. Each phase simulates like a percolation of a liquid in a medium defining the percolation trajectories. The propagation from one pixel to another is taken as easy or difficult determined by the difference of the gray level intensities. Finally different parameters like fractality from fractal dimensions, mean histogram etc associated to these trajectories are derived, based on which the primary experiment for the classification of random texture is carried out determining the relevance of this idea. Obviously, our study is only first approach and requires additional workout to obtain a reliable method of classification

En fait, le concept de texture n’est pas facile à définir, mais il est clair qu’il est fortement lié au Système Visuel Humain. Sachant que le Modèle LIP est compatible avec la vision humaine, il nous a semblé intéressant de créer des outils logarithmiques dédiés à l’évaluation de la texture. Nous nous sommes concentrés sur la notion de covariogramme, qui peut être pilotée par diverses métriques logarithmiques. Ces métriques jouent le rôle d’outils de “corrélation”, avec l’avantage de prendre en compte la vision humaine. De plus, les outils LIP sont peu dépendants des conditions d’éclairement et fournissent donc des résultats robustes si celles-ci varient. Les deux derniers Chapitres proposent une nouvelle approche consistant à considérer les niveaux de gris d’une image comme les phases d’un milieu. Chaque phase permet de simuler la percolation d’un liquide dans le milieu, définissant ainsi des trajectoires de percolation. Chaque propagation d’un pixel à un autre est considérée comme facile ou non, en fonction des niveaux de gris traversés. Une « fonction de coût » est créée, qui modifie le « temps » de propagation d’un point à l’autre. De plus, la fonction de coût peut être calculée dans le contexte LIP, pour prendre en compte la vision humaine / This thesis looks at the evaluation of textures in two different perspectives using logarithmic image processing (LIP) framework. The first case after introducing the concept of textures and giving some classical approaches of textures evaluation, it gives an original approach of textures evaluation called covariogram which is derived from similarity metrics like distances or correlations etc. The classical covariogram which is derived from the classical similarity metrics and LIP covariogram are then applied over several images and the efficiency of the LIP one is clearly shown for darkened images. The last two chapters offer a new approach by considering the gray levels of an image as the phases of a medium. Each phase simulates like a percolation of a liquid in a medium defining the percolation trajectories. The propagation from one pixel to another is taken as easy or difficult determined by the difference of the gray level intensities. Finally different parameters like fractality from fractal dimensions, mean histogram etc associated to these trajectories are derived, based on which the primary experiment for the classification of random texture is carried out determining the relevance of this idea. Obviously, our study is only first approach and requires additional workout to obtain a reliable method of classification