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Proceedings Paper

Principal components analysis: extension to scaling for application to images acquired at various ranges
Author(s): Gerald Cook; Gary F. O'Brien
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Paper Abstract

This paper builds on the method of Principal Components Analysis and its use for obtaining from a set of training image vectors a basis in which the members are rank ordered in terms of importance. The particular focus of this research is situations where the training vectors arise from images acquired at one range, call it the base range, and the image under question has been acquired at a different range. A natural question is whether one must train for all possible ranges. It is shown that, under certain assumptions, the eigenvectors for the data corresponding to ranges other than the base range may be approximated by performing a simple transformation on the eigenvectors derived from the training set at the base range. This is an important result, tending to obviate the need for acquisition of additional training patterns or for additional complex computations. It is also shown that under these assumptions the eigenvalues remain approximately constant over the different ranges even though pixel size is changed. Bounds for the errors of approximation introduced by the method are derived.

Paper Details

Date Published: 29 September 1999
PDF: 11 pages
Proc. SPIE 3810, Radar Processing, Technology, and Applications IV, (29 September 1999); doi: 10.1117/12.364066
Show Author Affiliations
Gerald Cook, George Mason Univ. (United States)
Gary F. O'Brien, U.S. Army Night Vision & Electronic Sensors Directorate (United States)

Published in SPIE Proceedings Vol. 3810:
Radar Processing, Technology, and Applications IV
William J. Miceli, Editor(s)

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