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Proceedings Paper

Scalable information-optimal compressive target recognition
Author(s): Ronan Kerviche; Amit Ashok
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Paper Abstract

We present a scalable information-optimal compressive imager optimized for the target classification task, discriminating between two target classes. Compressive projections are optimized using the Cauchy-Schwarz Mutual Information (CSMI) metric, which provides an upper-bound to the probability of error of target classification. The optimized measurements provide significant performance improvement relative to random and PCA secant projections. We validate the simulation performance of information-optimal compressive measurements with experimental data.

Paper Details

Date Published: 20 May 2016
PDF: 6 pages
Proc. SPIE 9870, Computational Imaging, 987008 (20 May 2016); doi: 10.1117/12.2228570
Show Author Affiliations
Ronan Kerviche, College of Optical Sciences, The Univ. of Arizona (United States)
Amit Ashok, College of Optical Sciences, The Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 9870:
Computational Imaging
Abhijit Mahalanobis; Kenneth S. Kubala; Amit Ashok; Jonathan C. Petruccelli; Lei Tian, Editor(s)

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