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dc.contributor.authorKerviche, Ronan
dc.contributor.authorAshok, Amit
dc.date.accessioned2016-12-08T01:36:31Z
dc.date.available2016-12-08T01:36:31Z
dc.date.issued2016-05-20
dc.identifier.citationScalable information-optimal compressive target recognition ", Proc. SPIE 9870, Computational Imaging, 987008 (May 20, 2016); doi:10.1117/12.2228570; http://dx.doi.org/10.1117/12.2228570en
dc.identifier.issn0277-786X
dc.identifier.doi10.1117/12.2228570
dc.identifier.urihttp://hdl.handle.net/10150/621547
dc.description.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.
dc.language.isoenen
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2228570en
dc.rights© 2016 SPIEen
dc.subjectCompressive Imagingen
dc.subjectClassificationen
dc.subjectCauchy-Schwarz Mutual Informationen
dc.subjectTarget Recognitionen
dc.titleScalable information-optimal compressive target recognitionen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Coll Opt Scien
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen
dc.identifier.journalCOMPUTATIONAL IMAGINGen
dc.description.collectioninformationThis 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 repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionCollege of Optical Sciences, The Univ. of Arizona (United States)
dc.contributor.institutionCollege of Optical Sciences, The Univ. of Arizona (United States)
refterms.dateFOA2018-06-24T11:03:28Z
html.description.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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