Information-theoretic bounds on target recognition performance

Avinash Jain, Pierre Moulin, Michael I. Miller, Kannan Ramchandran

Research output: Contribution to journalConference articlepeer-review


This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information-theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Applications to target recognition based on compressed sensor image data are given. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models, and optimizing system parameters.

Original languageEnglish (US)
Pages (from-to)347-358
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2000
Externally publishedYes
EventAutomatic Target Recognition X - Orlando, FL, USA
Duration: Apr 26 2000Apr 28 2000

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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