Adaptive search for morphological feature detectors
Mateen M. Rizki, Louis A. Tamburino, Michael A. Zmuda · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
A closed-loop hybrid learning system that facilitates the automatic design of a multi-class pattern recognition system is described. The design process has three phases: feature detector generation feature set selection and classification. In the first phase a large population of feature detectors based on morphological erosion and hit-or-miss operators is generated randomly. From this population an optimized subset of features is selected using a novel application of genetic algorithms. The selected features are then used to initialize a generalized Hamming neural network that performs image classification. This network provides the means for self-organizing the set of training patterns into additional subclasses this in turn dynamically alters the number of detectors and the size of the neural network. The design process uses system errors to gradually refine the set of feature vectors used in the classification subsystem. We describe an experiment in which the hybrid learning paradigm successfully generates a machine that distinguishes ten classes of handprinted numerical characters.