Evening lecture: 23 November
2004, at 18.15
Organizer: Australian
Statistical Society Inc.
Venue: Swinburne
University of Technology, Hawthorn
Campus, Melbourne, Australia
Topic:
Shape Understanding System: application of statistical
methods in designing of the system with the visual thinking capabilities
Zbigniew
LES and Magdalena
LES
The
Queen Jadwiga Research Institute of Understanding
Australia
The aim of this presentation
is to show the application of statistical methods in implementing of
shape understanding system (SUS). The application
of visual inference in selected areas of statistics will be also presented. Visual inference and visual reasoning
are part of the visual thinking capabilities of the shape understanding
system (SUS). Visual reasoning involves transformation of the object
description when passing stages of the reasoning process. SUS is an example
of the visual understanding system where sensory information is transformed
into the multilevel representation in the concept formation process that is
part of the visual thinking capabilities. Understanding requires
interpreting 2D images as the real world objects, symbols and signs.
Understanding is based on a large number of highly varied abilities called
intelligence that can be measured. Abilities of SUS to understand were tested based on the
methods used in the intelligence tests. It was
shown that SUS was able to express the visual perceptual data in the
form of the linguistic expressions. SUS performs visual reasoning based on
the shape category and visual inference based on combining the rules in
which the visual concept is embedded.
In this presentation the
focus is on the issues connected with shape, understanding, reasoning and
statistical methods. In the first part of the presentation the general
concepts such as understanding, visual thinking and reasoning will be
presented. The basic concepts of shape understanding method and selected
issues of the implementation of the shape understanding system (SUS) will
be introduced. In the second part, the statistical shape theory as well as
selected statistical methods applied in the pattern recognition and expert
systems such as Hidden Markov Models or Bayesian networks, will be briefly
discussed. The visual inference that is applied to solve the problem of
curve identification, a graphical investigation of the characteristic
points of the curve, visual tests, and identification of statistical visual
objects will be presented. An example of application of the SUS in
statistics (the regression analysis, cluster analysis, discriminant
analysis) will be presented.
Back
Home
