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Proceedings Paper

A subspace learning approach to evaluating the performance of image fusion algorithms
Author(s): Kenneth A. Byrd; Harold Szu; Mohamed Chouikha
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Paper Abstract

The fusion of multi-spectral images is an important pre-processing operation for scientists and engineers seeking to design robust detection, recognition and identification (DRI) systems. Due to the multitude of pixellevel fusion algorithms available, there is an extreme need for reliable metrics to analyze their performance. Most recently, subspace learning methods have been applied to the field of information fusion for object recognition and classification. This paper aims to extend the capabilities of existing nonlinear dimensionality reduction algorithms to a new area, evaluating the performance of image fusion algorithms. We prove that distances between points in the low dimensional embedding are essentially equivalent to the results given by estimating the amount of information transfered from source images to resultant fused images (normalized mutual information).

Paper Details

Date Published: 13 April 2010
PDF: 8 pages
Proc. SPIE 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII, 770310 (13 April 2010); doi: 10.1117/12.855787
Show Author Affiliations
Kenneth A. Byrd, U.S. Army RDECOM CERDEC NVESD (United States)
Howard Univ. (United States)
Harold Szu, U.S. Army RDECOM CERDEC NVESD (United States)
Mohamed Chouikha, Howard Univ. (United States)


Published in SPIE Proceedings Vol. 7703:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII
Harold H. Szu; F. Jack Agee, Editor(s)

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