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Proceedings Paper

Information Fusion Methodology
Author(s): Gerald M Flachs; Jay B Jordan; Jeffrey J Carlson
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Paper Abstract

An approach is presented for designing multisensor electronic vision systems using information fusion concepts. A random process model of the multisensor scene environment provides a mathematical foundation for fusing information. A complexity metric is introduced to measure the level of difficulty associated with various vision tasks. This complexity metric provides a mathematical basis for fusing information and selecting features to minimize the complexity metric. A major result presented in the paper is a method for utilizing a priori knowledge to fuse an n-dimensional feature vector X = (X1, X2, ..., Xn) into a single feature Y while retaining the same complexity. A fusing theorem is presented that defines the class of fusing functions that retains the minimum complexity.

Paper Details

Date Published: 9 August 1988
PDF: 8 pages
Proc. SPIE 0931, Sensor Fusion, (9 August 1988); doi: 10.1117/12.946648
Show Author Affiliations
Gerald M Flachs, New Mexico State University (United States)
Jay B Jordan, New Mexico State University (United States)
Jeffrey J Carlson, New Mexico State University (United States)

Published in SPIE Proceedings Vol. 0931:
Sensor Fusion
Charles B. Weaver, Editor(s)

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