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Proceedings Paper

Distance measures and match complexity for reconstructable pattern segmentations
Author(s): O. Scott Sands; Frederick D. Garber
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Paper Abstract

An abstraction of the structural pattern recognition problem is presented in which matches between observed and known segmented patterns are formed by minimizing a match distance function. Match distance is defined in terms of a fidelity measure of the reconstructions of pattern vectors from their segmented representations. Using this formulation it is shown that when reconstruction error is used to formulate match distance and the reconstruction distance measure is metric, then the structural classifier will be forced to form a trivial match between elements of the segmented patterns. Convergence properties of matching algorithms based on modified distance measures are described.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); doi: 10.1117/12.138291
Show Author Affiliations
O. Scott Sands, Naval Undersea Warfare Ctr. (United States)
Frederick D. Garber, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 1700:
Automatic Object Recognition II
Firooz A. Sadjadi, Editor(s)

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