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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.

 

 

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