Abstract-
This talk will be focused on the characteristics and properties
of some Markov random fields (MRFs) that seem well suited to
the processing of three-dimensional (3-D) signals.
First, the 3-D segmentation problem using n-ary unilateral
MRF models will be addressed. Theoretical properties
relating the stationarity to the unilateral factorization of
such fields will be presented. Then, we will show how a
simple parameterization of such fields can be used to derive
a numerically efficient unsupervised 3-D segmentation
method.
Second, the 3-D restoration problem will be considered. We
will present numerically efficient method, based on a
straightforward 3-D extension of "edge-preserving" convex
potential MRFs and on the maximization of the joint a
posteriori likelihood using the duality principle. In
addition, we will show how point spread functions with large
support regions can be efficiently accounted for.
In both cases, applications to 3-D medical data will be
presented, and the advantages, limitations and open problems
will be discussed.
Biosketch-
Yves Goussard was born in Paris, France, in 1957. He
graduated from Ecole Nationale Superieure de Techniques
Avancees in
1980, and he received the Doc. Ing. and Ph.D. degrees from
the Universite de Paris-Sud, Orsay, France, in 1983 and
1989, respectively.
From 1983 to 1985, he was a visiting scholar at the
Electrical Engineering and Computer Science Department of
the University of California, Berkeley. In 1985, he was
appointed a Charge de Recherche at CNRS, Gif-sur-Yvetts now
an Associate Professor. During the academic year 1990-1991,
he was on sabbatical leave at the Department of Electrical
Engineering-Systems, University of Southern California, Los
Angeles. After some work on nonlinear system identification
and modeling, his interests moved toward ill-posed problems
in signal and image processing with application to
biological systems.
To link to Dr. Goussard's Home Page just click here