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Proceedings Paper

On the use of diffusion maps for image fusion
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Paper Abstract

This work considers the problem of combining high dimensional data acquired from multiple sensors for the purpose of detection and classification. The sampled data are viewed as a geometric object living in a highdimensional space. Through an appropriate, distance preserving projection, those data are reduced to a lowdimensional space. In this reduced space it is shown that different physics of the sampled phenomena reside on different portions of the resulting "manifold" allowing for classification. Moreover, we show that data acquired from multiple sources collected from the same underlying physical phenomenon can be readily combined in the low-dimensional space i.e. fused. The process is demonstrated on maritime imagery collected from a visible-band camera.

Paper Details

Date Published: 5 May 2011
PDF: 6 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501D (5 May 2011); doi: 10.1117/12.884266
Show Author Affiliations
C. C. Olson, U.S. Naval Research Lab. (United States)
J. M. Nichols, U.S. Naval Research Lab. (United States)
K. P. Judd, U.S. Naval Research Lab. (United States)
F. Bucholtz, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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