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Ph.D. Theses


  • @phdthesis{Squ1996,
    	vgclass =	{thesis},
    	vgproject =	{nn,invariance},
    	author =	{David McG. Squire},
    	title =	{Model-based Neural Networks for Invariant Pattern
    	school =	{School of Computing, Curtin University of Technology},
    	address =	{Perth, Western Australia},
    	month =	{October},
    	year =	{1996},
    	note =	{(Submitted 1996, awarded March 19th, 1997)},
    	url =	{/publications/thesis.html},
    	abstract =	{In this thesis, the notion of \emph{Model-Based Neural
    	Networks} is introduced. Model-Based Neural Networks, whilst retaining
    	the essential structure and advantages of traditional neural networks,
    	enable explicit modeling of the target system. This can result in
    	dramatically improved generalization of classification performance to
    	patterns not present in the training data, and representation in
    	considerably lower-dimensional state spaces.
    	An important problem in many areas of computer vision is invariant
    	pattern recognition. This is the chosen domain for demonstrating the
    	efficacy of the Model-Based Neural Network approach. Model-Based Neural
    	Networks can be constructed which have responses which are invariant to
    	specified transformations of the input data. Such networks can be
    	trained with much smaller training sets than are required by
    	traditional networks, since it is no longer necessary to provide
    	examples of transformed versions of the input prototypes. This, coupled
    	with the reduction in the dimensionality of the parameter space, means
    	that training such networks is often much less
    	computationally-expensive than the traditional alternative.
    	To situate this work, a review of existing general techniques for
    	invariant pattern recognition is presented in Chapter~2, and of
    	previous neural network-based approaches in Chapter~3. Chapter~4
    	presents a variety of different forms of Model-Based Neural Network,
    	and demonstrates their utility on a range of invariant pattern
    	recognition problems, both real and synthetic. Included is a comparison
    	with an earlier study, which reveals the great improvements possible
    	with Model-Based Neural Networks.
    	Chapter~5 introduces a new invariant feature of two-dimensional
    	contours, the Invariance Signature. It is shown in Chapter~6 that a
    	Model-Based Neural Network can be constructed which calculates this
    	multi-dimensional feature, and classifies patterns on this basis.
    	Chapter~7 reports experimental results demonstrating that such
    	Invariance Signature-based MBNNs can be employed successfully for
    	shift-, rotation- and scale-invariant optical character recognition.},