Automatic target recognition of 3D objects under photon starved condition using advanced correlation filters

Abstract

In this paper, an overview of automatic target recognition for three-dimensional (3D) passive photon counting integral imaging system using maximum average correlation height filters is presented. Poisson distribution is adapted for generation photon counting images. For estimation of the 3D scene from photon counting images, maximum likelihood estimation is used. The advanced correlation filter is synthesized with ideal training images. Using this filter, we prove that automatic target recognition may be implemented under photon starved conditions. Since integral imaging may reduce the effect of occlusion and obscuration, the advanced correlation filter may detect and recognize a 3D object under photon starved environment. To demonstrate the ability of 3D photon counting automatic target recognition, experimental results are presented.