Subject: Lectures announcement

From: Laurence Leherte <[email-masked]>
Date: Wed, 04 Nov 92 10:08:38 +01

Announcement:
A Series of Lectures in the field of Chemistry and Artificial
Intelligence will be held at the Facultes Universitaires Notre-Dame
de la Paix (Namur, Belgium), on November the 19th.

                *****************************
                *  MOLECULAR SCENE ANALYSIS *
                *****************************

    Janice Glasgow, Dept. of Computing and Information Science
             Queen's University, Kingston, Canada

       Frank H. Allen, Cambridge Structural Data Centre,
                      Cambridge, UK.

    Suzanne Fortier, Dept. of Chemistry, Queen's University,
             Queen's University, Kingston, Canada

   The concept of "scene analysis" has been used in the context of
machine vision to refer to the set of processes associated with
the classification and understanding of complex images. Such
analyses rely on the availibility of domain knowledge in the form
of structural templates, rules or heuristics to locate and identify
features in a scene. By analogy we use the phrase "molecular scene
analysis" to refer to the processes associated with the reconstruction
and interpretation of molecular structures and molecular interactions.
This presentation, in three parts, will describe some fundamental
aspects of the Molecular Scene Analysis project.

Computational Imagery :  Janice Glasgow
---------------------------------------
   At the core of our knowledge-based approach to molecular
scene analysis is the concept of imagery, that is the ability to
reason with three-dimensional images of molecular structure.
To provide a computational framework that can imitate the
human visualization abilities, we are designing
image representations that make explicit the fundamental
spatial and visual charactersitics of a molecular scene.
Applying the computational reasoning techniques of molecular
imagery to the information accumulated in a crystallographic
knowledge base provides the "intelligence" vital to
molecular scene analysis.

From Databases to Knowledge Bases : Frank Allen

Conceptual Clustering Applications to Crystallographic Data :
----------------------------------------------------  Suzanne Fortier
                                                    -----------------
   Our research in machine learning is motivated by the need for
techniques to structure, manage and compress the rapidly growing
crystallographic databases and transform them into knowledge bases.
An incremental conceptual clustering algorithm, specifically designed
for objects/scenes composed of many parts, has been designed and
implemented. The algorithm and an initial application to pyranose sugar
data will be described.

Location:      Facultes Universitaires Notre-Dame de la Paix
---------      Chemistry Department
               Auditorium CH2
               Rue Grafe, 2
               B-5000 NAMUR
               Belgium

Schedule:      November, the 19th, 1992
---------      15:00

Information:   Prof. D. P. Vercauteren
------------   Dr. L. Leherte
               E. Titeca
               Laboratoire de Physico-Chimie Informatique
               email:vercau,leherte,titeca at scf.fundp.ac.be
               Tel:+32-81-724534, +32-81-724535
               Fax:+32-81-724530
Acknowledge-To: <LEHERTE@BNANDP11>