Neural network post-processing of grayscale optical correlator

Abstract

In real-world pattern recognition applications, multiple correlation filters can be synthesized to recognize broad variation of object classes, viewing angles, scale changes, and background clutters. Composite filters are used to reduce the number of filters needed for a particular target recognition task. Conventionally, the correlation peak is thresholded to determine if a target is present. Due to the complexity of the objects and the unpredictability of the environment, false positive or false negative identification often occur. In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.