Genetic Programming for Feature Detection and Image
Segmentation
R. Poli. Evolutionary Computing, 1143, Springer-Verlag, University of Sussex, UK, (1-2 April 1996)
Abstract
Genetic Programming is a method of program
discovery/optimisation consisting of a special kind of
genetic algorithm capable of operating on non-linear
chromosomes (parse trees) representing programs and an
interpreter which can run the programs being optimised.
In this paper we describe a set of terminals and
functions for the parse trees handled by genetic
programming which enable it to develop effective image
filters. These filters can either be used to highly
enhance and detect features of interest or to build
pixel-classification-based segmentation algorithms.
Some experiments with medical images which show the
efficacy of the approach are reported.
%0 Book Section
%1 poli:1996:GPfdis
%A Poli, Riccardo
%B Evolutionary Computing
%C University of Sussex, UK
%D 1996
%E Fogarty, T. C.
%I Springer-Verlag
%K algorithms, genetic programming
%N 1143
%P 110--125
%T Genetic Programming for Feature Detection and Image
Segmentation
%U http://citeseer.ist.psu.edu/334104.html
%X Genetic Programming is a method of program
discovery/optimisation consisting of a special kind of
genetic algorithm capable of operating on non-linear
chromosomes (parse trees) representing programs and an
interpreter which can run the programs being optimised.
In this paper we describe a set of terminals and
functions for the parse trees handled by genetic
programming which enable it to develop effective image
filters. These filters can either be used to highly
enhance and detect features of interest or to build
pixel-classification-based segmentation algorithms.
Some experiments with medical images which show the
efficacy of the approach are reported.
%@ 3-540-61749-3
@incollection{poli:1996:GPfdis,
abstract = {Genetic Programming is a method of program
discovery/optimisation consisting of a special kind of
genetic algorithm capable of operating on non-linear
chromosomes (parse trees) representing programs and an
interpreter which can run the programs being optimised.
In this paper we describe a set of terminals and
functions for the parse trees handled by genetic
programming which enable it to develop effective image
filters. These filters can either be used to highly
enhance and detect features of interest or to build
pixel-classification-based segmentation algorithms.
Some experiments with medical images which show the
efficacy of the approach are reported.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {University of Sussex, UK},
author = {Poli, Riccardo},
biburl = {https://www.bibsonomy.org/bibtex/2c2b6cfcd88885e4bfc77bdfad710f33f/brazovayeye},
booktitle = {Evolutionary Computing},
editor = {Fogarty, T. C.},
interhash = {28805a1dd404646eff2bb0a49eeb9ebc},
intrahash = {c2b6cfcd88885e4bfc77bdfad710f33f},
isbn = {3-540-61749-3},
keywords = {algorithms, genetic programming},
month = {1-2 April},
notes = {The post-workshop proceedings of the 1996 AISB
workshop on evolutionary computing.},
number = 1143,
pages = {110--125},
publisher = {Springer-Verlag},
series = {Lecture Notes in Computer Science},
size = {16 pages},
timestamp = {2008-06-19T17:49:37.000+0200},
title = {Genetic Programming for Feature Detection and Image
Segmentation},
url = {http://citeseer.ist.psu.edu/334104.html},
year = 1996
}