Search results for key=SqC1995 : 1 match found.

1995

• @inproceedings{SqC1995,
  	vgclass =	{refpap},
  	vgproject =	{nn,invariance},
  	author =	{David McG. Squire and Terry M. Caelli},
  	title =	{Shift, Rotation and Scale Invariant Signatures for
  	Two-Dimensional Contours, in a Neural Network Architecture},
  	editor =	{Stephen W. Ellacott and John C. Mason and Iain J. Anderson},
  	booktitle =	{Mathematics of Neural Networks: Models Algorithms and
  	Applications},
  	address =	{Boston},
  	series =	{Statistics and OR},
  	pages =	{344--348},
  	publisher =	{Kluwer Academic Publishers},
  	month =	{July},
  	year =	{1995},
  	doi =	{https://doi.org/10.1007/978-1-4615-6099-9_60},
  	url =	{/publications/postscript/manna.ps.gz},
  	abstract =	{A technique for obtaining shift, rotation and scale
  	invariant signatures for two dimensional contours is proposed and
  	demonstrated. An \emph{invariance factor} is calculated at each point
  	by comparing the orientation of the tangent vector with vector fields
  	corresponding to the generators of Lie transformation groups for shift,
  	rotation and scaling.  The statistics of these invariance factors over
  	the contour are used to produce an \emph{invariance signature}.
  
  	This operation is implemented in a Model-Based Neural Network (MBNN),
  	in which the architecture and weights are parameterised by the
  	constraints of the problem domain. The end result after constructing
  	and training this system is the same as a traditional neural network: a
  	collection of layers of nodes with weighted connections. The design and
  	modeling process can be thought of as \emph{compiling} an invariant
  	classifier into a neural network.  We contend that these invariance
  	signatures, whilst not unique, are sufficient to characterise contours
  	for many pattern recognition tasks.},
  }