Share Email Print

Proceedings Paper

Information fusion benefits delineation in off-nominal scenarios
Author(s): Belur V. Dasarathy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The potential problem of deterioration in recognition system performance because of imprecise, incomplete or imperfect training is a serious challenge inherent to most-real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the result of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in-decision out (FEI-DEO) fusion as well as decisions in-decision out fusion (DEI-DEO). The latter spans across both information sources and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in carious levels of acoustical noise.

Paper Details

Date Published: 12 March 1999
PDF: 12 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341330
Show Author Affiliations
Belur V. Dasarathy, Dynetics, Inc. (United States)

Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top