A well-ordered feature space mapping for sensor fusion

Gerald M. Flachs; Cynthia L. Beer; David R. Scott

doi:10.1117/12.960490

A well-ordered feature space mapping for sensor fusion

Gerald M. Flachs, Cynthia L. Beer, David R. Scott

Informatics, Computing, and Cyber Systems, School of

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	152-161
Number of pages	10
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	1100
DOIs	https://doi.org/10.1117/12.960490
State	Published - Sep 14 1989
Event	Sensor Fusion II 1989 - Orlando, United States Duration: Mar 27 1989 → Mar 31 1989

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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AB - 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.

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A well-ordered feature space mapping for sensor fusion

Abstract

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