Search results for key=Squ1996 : 1 match found.

1996

• @phdthesis{Squ1996,
  	vgclass =	{thesis},
  	vgproject =	{nn,invariance},
  	author =	{David McG. Squire},
  	title =	{Model-based Neural Networks for Invariant Pattern
  	Recognition},
  	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.},
  }