Asymptotic Performance of ATR in Infrared Images

Can Ceritoglu, Dmitri Bitouk, Michael I. Miller, Harry A. Schmitt

Research output: Contribution to journalConference articlepeer-review


In this study, the asymptotic performance analysis for target detection-identification through Bayesian hypothesis testing in infrared images is presented. In the problem, probabilistic representations in terms of Bayesian pattern-theoretic framework is used. The infrared clutter is modelled as a second-order random field. The targets are represented as rigid CAD models. Their infinite variety of pose is modelled as transformations on the templates. For the template matching in hypothesis testing, a metric distance, based on empirical covariance, is used. The asymptotic performance of ATR algorithm under this metric and Euclidian metric is compared. The receiver operating characteristic (ROC) curves indicate that using the empirical covariance metric improves the performance significantly. These curves are also compared with the curves based on analytical expressions. The analytical results predict the experimental results quite well.

Original languageEnglish (US)
Pages (from-to)109-118
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Dec 1 2003
EventPROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: Automatic Target Recognition XIII - Orlando, FL, United States
Duration: Apr 22 2003Apr 24 2003


  • Asymptotic performance
  • ATR
  • Hypothesis testing
  • Infrared image

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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