Scalable information-optimal compressive target recognition

Ronan Kerviche, Amit Ashok

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


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
ISBN (Electronic)9781510601116
StatePublished - 2016
EventComputational Imaging - Baltimore, United States
Duration: Apr 17 2016Apr 18 2016

Publication series

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


OtherComputational Imaging
Country/TerritoryUnited States


  • 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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