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Proceedings Paper

Minimum reconstruction error in feature-specific imaging
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Paper Abstract

We describe theoretical and experimental results for a new class of optimal features for feature-specific imaging (FSI). In this paper, we theoretically solve the reconstruction problem without noise, and find a more general solution than principle component analysis (PCA). We present a generalized framework to find FSI projection matrices. Using Stochastic Tunneling, we find an optimal solution in the presence of noise and under an energy conservation constraint. We also show that a non-negativity requirement does not significantly reduce system performance. Finally, we propose an experimental system for FSI using a polarization-based optical pipeline processor.

Paper Details

Date Published: 25 May 2005
PDF: 6 pages
Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005); doi: 10.1117/12.603059
Show Author Affiliations
Jun Ke, Univ. of Arizona (United States)
Michael D. Stenner, Univ. of Arizona (United States)
Mark A. Neifeld, Optical Sciences Ctr./Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 5817:
Visual Information Processing XIV
Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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