SELF ADAPTIVE HIERARCHICAL TARGET IDENTIFICATION AND RECOGNITION NEURAL NETWORK A self adaptive hierarchical target identification neural network pattern recognition system (10) is constructed utilizing four basic modules. The first module, is a segmenter and preprocessor (14) which accepts gray level image data (12) and is based on the Boundary Contour System neural network. The segmenter and preprocessor (14) output is fed to a feature extractor (16) which comprises a first layer of a Neocognitron. The feature extractor (16) output is fed to a pattern recognizer (18) which comprises layers 2 and 3 of the Neocognitron. The pattern recognizer (18) produces as output a real valued vector representation which encodes the object to be identified. This vector representation is fed to a classifier (20) which comprises a backpropagation neural network. The pattern recognition system (10) can classify large numbers of objects from raw sensor data and is relatively translation, rotation and scale invariant.
SELF ADAPTIVE HIERARCHICAL TARGET IDENTIFICATION AND RECOGNITION NEURAL NETWORK