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Proceedings Paper

Adaptive feature-specific spectral imaging
Author(s): P. A. Jansen; M. J. Dunlop; D. R. Golish; M. E. Gehm
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Paper Abstract

We present an architecture for rapid spectral classification in spectral imaging applications. By making use of knowledge gained in prior measurements, our spectral imaging system is able to design adaptive feature-specific measurement kernels that selectively attend to the portions of a spectrum that contain useful classification information. With measurement kernels designed using a probabilistically-weighted version of principal component analysis, simulations predict an orders-of-magnitude reduction in classification error rates. We report on our latest simulation results, as well as an experimental prototype currently under construction.

Paper Details

Date Published: 8 June 2012
PDF: 6 pages
Proc. SPIE 8365, Compressive Sensing, 83650B (8 June 2012); doi: 10.1117/12.918856
Show Author Affiliations
P. A. Jansen, Univ. of Arizona (United States)
M. J. Dunlop, Univ. of Arizona (United States)
D. R. Golish, Univ. of Arizona (United States)
M. E. Gehm, Univ. of Arizona (United States)
College of Optical Sciences, The Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 8365:
Compressive Sensing
Fauzia Ahmad, Editor(s)

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