Feature space trajectory neural net classifier: 8-class distortion-invariant tests

A novel neural network for distortion-invariant pattern recognition is described. Image regions of interest are determined using a detection stage, each region is then enhanced (the steps used are detailed), features are extracted (new Gabor wavelet features are used), and these features are used to classify the contents of each input region. A new feature space trajectory neural network (FST NN) classifier is used. A new 8 class database is used, a new multilayer NN to calculate the distance measures necessary is detailed, its low storage and on-line computational load requirements are noted. The ability of the adaptive FST algorithm to reduce network complexity while achieving excellent performance is demonstrated. The clutter rejection ability of this neural network to reject false alarm inputs is demonstrated, and time-history processing to further reduce false alarms is discussed. Hardware and commercial realizations are noted.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.