Foveal automatic target recognition using a neural network

This paper proposes a method for identifying and classifying a target from its foveal imagery using a neural network. The method's criterion for identifying a target is based on finding the global minimum of an energy function. This energy function is characterized by matching the candidate target and a library of target models at several levels of resolution of nonuniformly sampled foveal image data. For this purpose, a top-down and bottom-up (concurrent) matching procedure is implemented via a multi-layer Hopfield neural network. The corresponding energy function supports not only connections between cells at the same resolution level, but also interconnections between two sets of nodes at two different resolution levels. The proposed method also utilizes feature analysis at the higher resolution levels of the target to relocate the center of the fovea to a more salient region of the target (gaze control). The results of an experimental scenario for foveal target recognition are presented.