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Proceedings Paper

Sensor fusion using K-nearest neighbor concepts
Author(s): David R. Scott; Gerald M. Flachs; Patrick T. Gaughan
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Paper Abstract

A new K-nearest neighbor (KNN) statistic is introduced to fuse information from multiple sensors/features into a single dimensional decision space for electronic vision systems. Theorems establish the relationship of the KNN statistic to other probability density function distance measures such as the Kolmogorov-Smirnov Distance and the Tie Statistic. A new KNN search algorithm is presented along with factors for selecting K. Applications include cueing and texture recognition.

Paper Details

Date Published: 1 April 1991
PDF: 12 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25272
Show Author Affiliations
David R. Scott, Northern Arizona Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)
Patrick T. Gaughan, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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