Refereed full papers (journals, book chapters, international conferences)

1997

@inbook{Squ1997a,
	vgclass =	{refpap},
	vgproject =	{nn,invariance},
	author =	{David McG. Squire},
	title =	{Invariance Signatures for two-dimensional contours},
	editor =	{Terry Caelli and Walter F. Bischof},
	booktitle =	{Machine Learning and Image Interpretation},
	address =	{New York},
	series =	{Advances in Computer Vision and Machine Intelligence,
	Series editor: Martin D. Levine},
	chapter =	{7},
	pages =	{255--308},
	publisher =	{Plenum Press},
	year =	{1997},
	doi =	{http://dx.doi.org/10.1007/978-1-4899-1816-1_7},
	abstract =	{Invariant pattern recognition is an important problem in
	many areas of computer vision. In this chapter, a new invariant feature
	of two-dimensional contours is introduced: the Invariance Signature
	(IS). The IS is a measure of the degree to which a contour is invariant
	under a variety of transformations, derived from the theory of Lie
	transformation groups. It is shown that a Model-Based Neural Network
	(MBNN) can be constructed which computes the IS of a contour, and
	classifies patterns on this basis. MBNNs, whilst retaining the
	structure and advantages of traditional neural networks (TNNs), enable
	explicit modeling of the target system. This can result in greatly
	improved generalization, and representation in lower-dimensional state
	spaces. MBNNs can be trained with much smaller training sets than are
	required by TNNs. This means that MBNNs are much less
	computationally-expensive to train than TNNs.  Experiments demonstrate
	that such Invariance Signature networks can be employed successfully
	for shift-, rotation- and scale-invariant optical character
	recognition.},
}