Encoding visual information using anisotropic transformations

The evolution of information in images undergoing fine-to-coarse anisotropi c transformations is analyzed by using an approach based on the theory of i rreversible transformations. In particular, we show that, when an anisotrop ic diffusion model is used, local variation of entropy production over spac e and scale provides the basis for a general method to extract relevant ima ge features.