Image Processing. A new Approach via Informational Entropy and Informational Divergence of non Random Functions

By combining a maximum conditional entropy principle with a basic equation of (Shannon) information theory, one can obtain a meaningful concept of informational entropy of non random functions. When this entropy is applied to the brightness function of an image, one so has at hand a new tool which provides new approaches to some image processing problems, such as, for instance, image representation, image compression and image similarity. As a by-product, to some extent, this new modelling provides a support to the so-called monkey model of image entropy. But while the latter involves the brightness itself, here, the entropy of the brightness function is expressed in terms of the contrast of the brightness instead of the brightness itself. In this framework, a new concept of informational divergence of an image is obtained, which could be of help in image analysis

Distributed modelling of deterministic and stochastic population dynamics

AbstractIn many problems of practical purpose, one is interested not only in the study of the total population of the overall system, but also in the distribution of the population depending upon some characteristic parameters. This necessity is patent in biological systems defined on a wide range of distributed features, but it is also in order when one tries to analyze social systems by means of population models. As a matter of fact, with this objective in mind, we deal with infinite species population models.This study discusses this question, proposes a distributed version for the logistic equation, and examines the existence of stationary solutions. The problem of the influence of environmental noise on the dynamics of the distributed population is considered, and a model is proposed. A detailed analysis is carried out around the stationary solution via a linearization technique, and the covariance equation is derived