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1997

David McG. Squire,
Invariance Signatures for twodimensional contours,
In Terry Caelli and Walter F. Bischof eds., Machine Learning and Image Interpretation,
New York, Advances in Computer Vision and Machine Intelligence,
Series editor: Martin D. Levine, 7, pp. 255308, Plenum Press, 1997.
Invariant pattern recognition is an important problem in
many areas of computer vision. In this chapter, a new invariant feature
of twodimensional contours is introduced: the Invariance Signature
(IS). The IS 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. It is shown that a ModelBased Neural Network
(MBNN) can be constructed which computes the IS of a contour, and
classifies patterns on this basis. MBNNs, whilst retaining the
structure and advantages of traditional neural networks (TNNs), enable
explicit modeling of the target system. This can result in greatly
improved generalization, and representation in lowerdimensional state
spaces. MBNNs can be trained with much smaller training sets than are
required by TNNs. This means that MBNNs are much less
computationallyexpensive to train than TNNs. Experiments demonstrate
that such Invariance Signature networks can be employed successfully
for shift, rotation and scaleinvariant optical character
recognition.
