Morphological shared-weight networks with applications to automatic target recognition

Abstract

A shared-weight neural network based on mathematical morphology is introduced. The feature extraction process is learned by interaction with the classification process. Feature extraction is performed using gray-scale hit-miss transforms that are independent of gray-level shifts. The morphological shared-weight neural network (MSNN) is applied to automatic target recognition. Two sets of images of outdoor scenes are considered. The first set consists of two subsets of infrared images of tracked vehicles. The goal in this set is to reject the background and to detect tracked vehicles. The second set consists of visible images of cars in a parking lot. The goal in this set is to detect the Chevrolet Blazers with various degrees of occlusion. A training method that is effective in reducing false alarms and a target aim point selection algorithm are introduced. The MSNN is compared to the standard shared-weight neural network. The MSNN trains relatively quickly and exhibits better generalization.