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Abstract

Synergetics is the study of systems in which individual sub-systems act co-operatively.
In particular, we are interested as to how this co-operation on a microscopic scale can
lead to the formation of macroscopic spatial structures or patterns.
Synergetic pattern recognition is a non-conventional form of pattern recognition
which is modelled on these synergetic pattern formation systems. Indeed the paradigm
of synergetic pattern recognition states that pattern recognition is a type of pattern
formation.
This thesis is split into two parts. In Part A we introduce synergetic pattern
recognition. A review of the current range of synergetic pattern recognition systems
leads to a recognition of two major weaknesses in the current crop of synergetic pattern
recognition algorithms, so we introduce a number of generalised pattern formation
models which extend the capabilities of synergetic pattern recognition.
We also investigate the concept of synergetic learning whereby learning is considered
as a type of pattern formation. During a review of the current approach to this
task we recognise a number of problems and solve them with an important observation
concerning the dependence between variables. The new learning algorithm which
results is a significant improvement over the current approach.
In Part B of this work, we discuss the possible application of synergetic pattern
recognition to the task of view-based pose estimation, which is the challenge of estimating
the angles at which an image has been rotated, without reference to a full
model of the object.
Synergetic pattern recognition has not been used to solve this problem previously,
so following a review of the view-based pose estimation literature, we introduce two new
approaches to pose estimation based on synergetic pattern recognition. Comparison
of the results found by the various techniques on a standard dataset reveal a number
of common issues, foremost among which is the need to make a compromise between
the precision of a system's estimate and the amount of time taken to produce the
estimate.
This observation leads to the most exciting finding of this work. We describe
a new approach, not just to view-based pose estimation, but to the general field of
pattern analysis, which we call explicit inversion. The reason for the name is that
we have replaced the problem of numerically inverting a high-dimensional, unknown
equation with analytically inverting a known, low-dimensional equation. Comparison
with other approaches shows that this new approach yields comparable accuracy to
current algorithms but with a dramatic reduction in calculation times. For datasets
used in this dissertation, the increase in speed was between one to two orders of magnitude.
We then apply our algorithms to the real-world task of tracking the pose of an
aircraft. While a number of attempts have been made previously, this is the only
approach which shows promise of being able to track aircraft pose in real-time.

Item Type:	Thesis - PhD
Authors/Creators:	Hogg, T
Keywords:	Pattern recognition systems, Estimation theory
Copyright Holders:	The Author
Copyright Information:	Copyright 1998 the Author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s).
Additional Information:	Thesis (Ph.D.)--University of Tasmania, 1998. Includes bibliographical references
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