Distance measures and match complexity for reconstructable pattern segmentations

O. Scott Sands; Frederick D. Garber

doi:10.1117/12.138291

16 September 1992 Distance measures and match complexity for reconstructable pattern segmentations

O. Scott Sands, Frederick D. Garber

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138291
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

O. Scott Sands and Frederick D. Garber "Distance measures and match complexity for reconstructable pattern segmentations", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138291

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