Morphological algorithm design for binary images using genetic programming

Authors:
Marcos I. Quintana;Riccardo Poli;Ela Claridge
Affiliations:
CIEP, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, San Luis Potosí S.L.P., México C.P. 78290;Department Computer Science, University of Essex, Colchester, UK CO4 3SQ;School of Computer Science, The University of Birmingham, Birmingham, UK B15 2TT
Venue:
Genetic Programming and Evolvable Machines
Year:
2006

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Abstract

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.