An Introduction to Pattern Recognition - Michael Alder.pdf

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An Introduction to
Pattern Recognition
Michael Alder
HeavenForBooks.com
An Introduction
to Pattern
Recognition
by
Michael Alder
HeavenForBooks.com
An Introduction to Pattern Recognition
This Edition ©Mike Alder, 2001
Warning: This edition is not to be
copied, transmitted excerpted or printed except
on terms authorised by the publisher
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An Introduction to Pattern Recognition: Statistical, Neural Net and Syntactic methods of getting robots to see and hear.
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Contents
An Introduction to Pattern Recognition:
Statistical, Neural Net and Syntactic
methods of getting robots to see and
hear.
Michael D. Alder
September 19, 1997
Preface
Automation, the use of robots in industry, has not progressed with the speed that many had hoped it
would. The forecasts of twenty years ago are looking fairly silly today: the fact that they were produced
largely by journalists for the benefit of boardrooms of accountants and MBA's may have something to do
with this, but the question of why so little has been accomplished remains.
The problems were, of course, harder than they looked to naive optimists. Robots have been built that
can move around on wheels or legs, robots of a sort are used on production lines for routine tasks such as
welding. But a robot that can clear the table, throw the eggshells in with the garbage and wash up the
dishes, instead of washing up the eggshells and throwing the dishes in the garbage, is still some distance
off.
Pattern Classification,
more often called
Pattern Recognition,
is the primary bottleneck in the task of
automation. Robots without sensors have their uses, but they are limited and dangerous. In fact one might
plausibly argue that a robot without sensors isn't a
real
robot at all, whatever the hardware manufacturers
may say. But equipping a robot with vision is easy only at the hardware level. It is neither expensive nor
technically difficult to connect a camera and frame grabber board to a computer, the robot's `brain'. The
problem is with the software, or more exactly with the
algorithms
which have to decide what the robot is
looking at; the input is an array of pixels, coloured dots, the software has to decide whether this is an
image of an eggshell or a teacup. A task which human beings can master by age eight, when they decode
the firing of the different light receptors in the retina of the eye, this is computationally very difficult, and
we have only the crudest ideas of how it is done. At the hardware level there are marked similarities
between the eye and a camera (although there are differences too). At the algorithmic level, we have only
a shallow understanding of the issues.
http://ciips.ee.uwa.edu.au/~mike/PatRec/ (1 of 11) [12/12/2000 4:01:56 AM]
An Introduction to Pattern Recognition: Statistical, Neural Net and Syntactic methods of getting robots to see and hear.
Human beings are very good at learning a large amount of information about the universe and how it can
be treated; transferring this information to a program tends to be slow if not impossible.
This has been apparent for some time, and a great deal of effort has been put into research into practical
methods of getting robots to recognise things in images and sounds. The Centre for Intelligent
Information Processing Systems (CIIPS), of the University of Western Australia, has been working in the
area for some years now. We have been particularly concerned with neural nets and applications to
pattern recognition in speech and vision, because adaptive or
learning
methods are clearly of great
potential value. The present book has been used as a postgraduate textbook at CIIPS for a Master's level
course in Pattern Recognition. The contents of the book are therefore oriented largely to image and to
some extent speech pattern recognition, with some concentration on neural net methods.
Students who did the course for which this book was originally written, also completed units in
Automatic Speech Recognition Algorithms, Engineering Mathematics (covering elements of Information
Theory, Coding Theory and Linear and Multilinear algebra), Artificial Neural Nets, Image Processing,
Sensors and Instrumentation and Adaptive Filtering. There is some overlap in the material of this book
and several of the other courses, but it has been kept to a minimum. Examination for the Pattern
Recognition course consisted of a sequence of four micro-projects which together made up one
mini-project.
Since the students for whom this book was written had a variety of backgrounds, it is intended to be
accessible. Since the major obstructions to further progress seem to be fundamental, it seems pointless to
try to produce a handbook of methods without analysis. Engineering works well when it is founded on
some well understood scientific basis, and it turns into alchemy and witchcraft when this is not the case.
The situation at present in respect of our scientific basis is that it is, like the curate's egg, good in parts.
We are solidly grounded at the hardware level. On the other hand, the software tools for encoding
algorithms (C, C++, MatLab) are fairly primitive, and our grasp of what algorithms to use is negligible. I
have tried therefore to focus on the ideas and the (limited) extent to which they work, since progress is
likely to require new ideas, which in turn requires us to have a fair grasp of what the old ideas are. The
belief that engineers as a class are not intelligent enough to grasp any ideas at all, and must be trained to
jump through hoops, although common among mathematicians, is not one which attracts my sympathy.
Instead of exposing the fundamental ideas in algebra (which in these degenerate days is less intelligible
than Latin) I therefore try to make them plain in English.
There is a risk in this; the ideas of science or engineering are quite diferent from those of philosophy (as
practised in these degenerate days) or literary criticism (ditto). I don't mean they are about different
things, they are different in kind. Newton wrote `Hypotheses non fingo', which literally translates as `I do
not make hypotheses', which is of course quite untrue, he made up some spectacularly successful
hypotheses, such as universal gravitation. The difference between the two statements is partly in the
hypotheses and partly in the fingo. Newton's `hypotheses' could be tested by observation or calculation,
whereas the explanations of, say, optics, given in Lucretius
De Rerum Naturae
were recognisably
`philosophical' in the sense that they resembled the writings of many contemporary philosophers and
literary critics. They may persuade, they may give the sensation of profound insight, but they do not
reduce to some essentially prosaic routine for determining if they are actually true, or at least useful.
Newton's did. This was one of the great philosophical advances made by Newton, and it has been
underestimated by philosophers since.
http://ciips.ee.uwa.edu.au/~mike/PatRec/ (2 of 11) [12/12/2000 4:01:56 AM]

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