16 September 2003 Asymptotic performance of ATR in infrared images
Author Affiliations +
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Can Ceritoglu, Can Ceritoglu, Dmitri Bitouk, Dmitri Bitouk, Michael I. Miller, Michael I. Miller, Harry A. Schmitt, Harry A. Schmitt, } "Asymptotic performance of ATR in infrared images", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.487383; https://doi.org/10.1117/12.487383


Back to Top