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.
%0 Journal Article
%1 Quintana:2006:GPEM
%A Quintana, Marcos I.
%A Poli, Riccardo
%A Claridge, Ela
%D 2006
%J Genetic Programming and Evolvable Machines
%K Image Mathematical algorithms, analysis, genetic memory morphology, programming,
%N 1
%P 81--102
%R doi:10.1007/s10710-006-7012-3
%T Morphological algorithm design for binary images using
genetic programming
%U http://cswww.essex.ac.uk/staff/rpoli/papers/gpem2005.pdf
%V 7
%X 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.
@article{Quintana:2006:GPEM,
abstract = {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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Quintana, Marcos I. and Poli, Riccardo and Claridge, Ela},
biburl = {https://www.bibsonomy.org/bibtex/23883f0152fd0fe2a396f49612c919a69/brazovayeye},
doi = {doi:10.1007/s10710-006-7012-3},
interhash = {d01a08ac84f56bcfb7bfbbef497080fd},
intrahash = {3883f0152fd0fe2a396f49612c919a69},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {Image Mathematical algorithms, analysis, genetic memory morphology, programming,},
month = {March},
notes = {Store operation used only in first part. lilgp. Linux
cluster. irregular kernels. Music score OCR.},
number = 1,
pages = {81--102},
size = {22 pages},
timestamp = {2008-06-19T17:49:58.000+0200},
title = {Morphological algorithm design for binary images using
genetic programming},
url = {http://cswww.essex.ac.uk/staff/rpoli/papers/gpem2005.pdf},
volume = 7,
year = 2006
}