Search results for key=SqC1997 : 1 match found.

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Technical Reports


  • 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.