SELF ADAPTIVE HIERARCHICAL TARGET IDENTIFICATION AND RECOGNITION NEURAL NETWORK

摘要

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.