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
