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Proceedings Paper

Utilization of Riemann surfaces in sensor data fusion
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Paper Abstract

The use of Riemann surfaces offers an alternative approach to the characterization of object classes. In a situation where data from multiple sensors is available, the sensor observables can be used as the coordinates which define the space occupied by the class surfaces. The curvature of the surfaces will be governed by the underlying correlations between the phenomenologies of interest and the viewing conditions. The result is a natural coordinate system in which to implement classification algorithms. In this paper, a simple two-dimensional example is presented which introduces the underlying mathematics of the approach. A traditional statistical classifier is then used to examine classification performance. Extending the approach to include non-collocated sensors, sensor measurement error, and noise sources is also briefly discussed.

Paper Details

Date Published: 30 October 1996
PDF: 11 pages
Proc. SPIE 2905, Sensor Fusion and Distributed Robotic Agents, (30 October 1996); doi: 10.1117/12.256324
Show Author Affiliations
Michael Patrick Cain, Booz, Allen & Hamilton, Inc. (United States)

Published in SPIE Proceedings Vol. 2905:
Sensor Fusion and Distributed Robotic Agents
Paul S. Schenker; Gerard T. McKee, Editor(s)

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