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.
This paper analyzes the performance of ATR algorithms in clutter. The variability of target type and pose is accommodated by introducing a deformable template for every target type, with low-dimensional groups of geometric transformations representing position and pose. Signature variation of targets is taken into account by expanding deformable templates into robust deformable templates generated from the template and a linear combination of PCA elements, spanning signature intensities. Detection and classification performance is characterized using ROC analysis. Asymptotic expressions for probabilities of recognition errors are derived, yielding asymptotic error rates. The results indicate that the asymptotic error probabilities depend upon a parameter, which characterizes the separation between the true target and the most similar but incorrect one. It is shown that the asymptotic expressions derived almost accurately predict performance of detection and identification of targets occluded by natural clutter.
Our work focuses on automated recognition of target obscured by clutter. Considering clutter as a filtered marked point Poisson process, a MMSE pose estimator of rigid objects in obscuring clutter is introduced. We also build asymptotic approximations for Bayesian posterior distributions based on the Fisher information. We relate the Fisher information to the Hilber-Schmidt pose estimator, the expected error of which is shown to be a lower bound on the error incurred by any other pose estimator.