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Proceedings Paper

Multiresolutional Sensor Fusion By Conductivity Analysis
Author(s): H. E. Stephanou; A. M. Erkmen
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Paper Abstract

This paper describes an evidential pattern classifier for the combination of data from physically different sensors. We assume that the sensory evidence is multiresolutional, incomplete, imprecise, and possibly inconsistent. Our focus is on two types of sensory information patterns: visual and tactile. We develop a logical sensing scheme by using a model based representation of prototypical 3D surfaces. Each surface represents a class of topological patterns described by shape and curvature features. The sensory evidence is classified by using a conductivity measure to determine which prototypical surface best matches the evidence. A formal evidential model of uncertainty is used to derive logical sensors and provide performance measures for sensor integration algorithms.

Paper Details

Date Published: 1 March 1990
PDF: 14 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969995
Show Author Affiliations
H. E. Stephanou, George Mason University (United States)
A. M. Erkmen, George Mason University (United States)


Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

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