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Proceedings Paper

Improved image segmentation using an inference fusion architecture
Author(s): Timothy M. Brucks; Jack G. Riddle; Peter J. Van Maasdam
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Paper Abstract

Image segmentation, a key component in many Automatic Target Recognition (ATR) systems, has received considerable attention in the research community in recent years. A variety of segmentation approaches exist, and attempts have been made to combine various approaches in order to find more robust solutions. In this paper, the authors describe an inference fusion architecture for combining individual segmentation concepts which results in improved performance over the individual algorithms. We consider segmentation algorithms with several disparate cost functions as experts with a narrowly defined set of goals. The information obtained from each expert is combined and weighted with available evidence using an agent based inference system, resulting in an adaptive, robust and highly flexible image segmentation. Results obtained by applying this approach will be presented.

Paper Details

Date Published: 1 April 2003
PDF: 9 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.485824
Show Author Affiliations
Timothy M. Brucks, Raytheon Missile Systems Co. (United States)
Jack G. Riddle, Raytheon Missile Systems Co. (United States)
Peter J. Van Maasdam, Raytheon Missile Systems Co. (United States)


Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

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