Genetic Programming for Feature Detection and Image Segmentation
Inhaltsverzeichnis
Reference
Riccardo Poli: Genetic Programming for Feature Detection and Image Segmentation. Evolutionary Computing, Lecture Notes in Computer Science, Number 1143, pp. 110-125, Springer-Verlag, 1-2 April 1996.
DOI
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. 1 Introduction Genetic Programming (GP) is the extension of Genetic Algorithms (GAs) in which the structures that make up the population under optimisation are not fixed-length character strings that encode possible solutions to a problem, but programs that, when executed, are the candidate solutions to the problem [1, 2].
Extended Abstract
Bibtex
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Links
Full Text
http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-AISB-1996.pdf (no c&p)
Sonstige Links
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.3791&rank=1
