AffiliationUniv Arizona, Coll Opt Sci
Univ Arizona, Dept Elect & Comp Engn
MetadataShow full item record
PublisherSPIE-INT SOC OPTICAL ENGINEERING
CitationScalable information-optimal compressive target recognition ", Proc. SPIE 9870, Computational Imaging, 987008 (May 20, 2016); doi:10.1117/12.2228570; http://dx.doi.org/10.1117/12.2228570
Rights© 2016 SPIE
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWe 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.
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