Search results for key=SqC1995 : 1 match found.

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  • @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
    	address =	{Boston},
    	series =	{Statistics and OR},
    	pages =	{344--348},
    	publisher =	{Kluwer Academic Publishers},
    	month =	{July},
    	year =	{1995},
    	doi =	{},
    	url =	{/publications/postscript/},
    	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.},