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Proceedings Paper

A Well-Ordered Feature Space Mapping For Sensor Fusion
Author(s): Gerald M. Flachs; Cynthia L. Beer; David R. Scott
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Paper Abstract

An approach is presented for mapping a multisensor feature space into a space that is well-ordered for vision tasks. A new statistic, the tie statistic (TS), is introduced for measuring the difference between two probability density functions (pdfs). The TS is related to the Kolmogorov-Smirnov statistic (KS) to demonstrate its ability to decide whether or not a sample came from a known pdf. The TS is used to map the measured feature space into a simplified decision space. In the mapping process, the tie statistic is itself a random variable that has a distribution that can be parametrically approximated by the Beta distribution. The tie mapping process is presented and applied to solve two important vision problems.

Paper Details

Date Published: 14 September 1989
PDF: 10 pages
Proc. SPIE 1100, Sensor Fusion II, (14 September 1989); doi: 10.1117/12.960490
Show Author Affiliations
Gerald M. Flachs, New Mexico State University (United States)
Cynthia L. Beer, New Mexico State University (United States)
David R. Scott, New Mexico State University (United States)

Published in SPIE Proceedings Vol. 1100:
Sensor Fusion II
Charles B. Weaver, Editor(s)

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