Shape analysis using morphological processing and genetic algorithms

A novel way of combining morphological processing and genetic algorithms (GAs) to generate high-performance shape discrimination operators is presented. GAs can evolve operators that discriminate among classes comprising different shapes. The operators are defined as variable structuring elements and can be sequenced as program forms. The population of such operators, evaluated according to an index of performance corresponding to shape discrimination ability, evolves into an optimal set of operators using genetic search. Experimental results are presented to illustrate the feasibility of the approach for shape discrimination