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Technical Reports
1997
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David McG. Squire and Terry M. Caelli,
Invariance Signatures: Characterizing contours by their
departures from invariance.
Tech. Rep. 97.04, Computer Vision Group, Computing Centre, University
of Geneva, rue Général Dufour, 24, CH-1211 Genève,
Switzerland, April 1997.
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.
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