Scalable information-optimal compressive target recognition

Ronan Kerviche, Amit Ashok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationComputational Imaging
EditorsKenneth S. Kubala, Lei Tian, Abhijit Mahalanobis, Amit Ashok, Jonathan C. Petruccelli
PublisherSPIE
ISBN (Electronic)9781510601116
DOIs
StatePublished - 2016
EventComputational Imaging - Baltimore, United States
Duration: Apr 17 2016Apr 18 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9870
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherComputational Imaging
CountryUnited States
CityBaltimore
Period4/17/164/18/16

Keywords

  • Cauchy-Schwarz Mutual Information
  • Classification
  • Compressive Imaging
  • Target Recognition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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