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( h , Φ ) -entropy differential metric

María Luisa Menéndez, Domingo Morales, Leandro Pardo, Miquel Salicrú (1997)

Applications of Mathematics

Burbea and Rao (1982a, 1982b) gave some general methods for constructing quadratic differential metrics on probability spaces. Using these methods, they obtained the Fisher information metric as a particular case. In this paper we apply the method based on entropy measures to obtain a Riemannian metric based on ( h , Φ ) -entropy measures (Salicrú et al., 1993). The geodesic distances based on that information metric have been computed for a number of parametric families of distributions. The use of geodesic...

( R , S ) -information radius of type t and comparison of experiments

Inder Jeet Taneja, Luis Pardo, D. Morales (1991)

Applications of Mathematics

Various information, divergence and distance measures have been used by researchers to compare experiments using classical approaches such as those of Blackwell, Bayesian ets. Blackwell's [1] idea of comparing two statistical experiments is based on the existence of stochastic transformations. Using this idea of Blackwell, as well as the classical bayesian approach, we have compared statistical experiments by considering unified scalar parametric generalizations of Jensen difference divergence measure....

4D Embryogenesis image analysis using PDE methods of image processing

Paul Bourgine, Róbert Čunderlík, Olga Drblíková-Stašová, Karol Mikula, Mariana Remešíková, Nadine Peyriéras, Barbara Rizzi, Alessandro Sarti (2010)

Kybernetika

In this paper, we introduce a set of methods for processing and analyzing long time series of 3D images representing embryo evolution. The images are obtained by in vivo scanning using a confocal microscope where one of the channels represents the cell nuclei and the other one the cell membranes. Our image processing chain consists of three steps: image filtering, object counting (center detection) and segmentation. The corresponding methods are based on numerical solution of nonlinear PDEs, namely...

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