1997
@techreport{SqC1997,
vgclass = {report},
vgproject = {nn,invariance},
author = {David McG. Squire and Terry M. Caelli},
title = {Invariance Signatures: Characterizing contours by their
departures from invariance},
number = {97.04},
institution = {Computer Vision Group, Computing Centre, University
of Geneva},
address = {rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve,
Switzerland},
month = {April},
year = {1997},
url = {/publications/postscript/1997/VGTR97.04_SquireCaelli.pdf},
url1 = {/publications/postscript/1997/VGTR97.04_SquireCaelli.ps.gz},
abstract = {In this paper, a new invariant feature of two-dimensional
contours is reported: the Invariance Signature. The Invariance
Signature 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. Since it is derived from local properties of the
contour, it is well-suited to a neural network implementation. It is
shown that a Model-Based Neural Network (MBNN) can be constructed which
computes the Invariance Signature of a contour, and classifies patterns
on this basis. Experiments demonstrate that Invariance Signature
networks can be employed successfully for shift-, rotation- and
scale-invariant optical character recognition.},
}