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  • @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 =	{},
    	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