Performance assessment of Unconstrained Hybrid Optical Neural Network (U-HONN) filter for object recognition tasks in clutter

Abstract

Previously we have described a hybrid optical neural network (HONN) filter. The filter is synthesised employing an artificial neural network technique that generates a non-linear interpolation of the intermediate train set poses of the training-set objects but maintains linear shift-invariance which allows potential implementation within a linear optical correlator type architecture. In this paper, we remove the constraints imposed on the filter’s output correlation peak height from the constraint matrix of the synthetic discriminant function used to create the composite filter. We examine the U-HONN filter’s detectability, peak sharpness, within-class distortion range, discrimination ability between an in-class and out-of-class object and the filter’s tolerance to clutter. We assess the behaviour of the U-HONN filter in an open area surveillance application. The filter demonstrates good object detection abilities within cluttered scenes, keeping good quality correlation peak sharpness and detectability throughout all the sets of tests. Thus the U-HONN filter is able to detect and accurately classify the in-class object within different background scenes at intermediate angles to the train-set poses.