Automatic construction of image operators using a genetic programming approach

This paper presents a methodology for automatic construction of image operators using a linear genetic programming approach, for binary, gray level and color image processing, where the processing solution for a particular application is expressed in terms of the basic morphological operators, dilation and erosion, in conjunction with convolution and logical operators. Genetic Programming (GP), based on concepts of genetics and Darwin's principle of natural selection, to genetically breed and evolve computer programs to solve a wide variety of problems, is a branch of evolutionary computation, and it is consolidating as a promising methodology to be used in applications involving pattern recognition, classification problems and modeling of complex systems. Mathematical morphology is based on the set theory (complete lattice), where the notion of order is very important. This processing technique has proved to be a powerful tool for many computer vision tasks. However, the manual design of complex operations involving image operators is not trivial in practice. Thus, the proposed methodology tries to solve these drawbacks. Some examples of applications are presented and the results are discussed and compared with other methods found in the literature.