Search results for key=Squ1997a : 1 match found.

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.},
  }