A Genetic Programming Approach for Image Segmentation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InCollection{Perlin:2013:CIIP,
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author = "Hugo Alberto Perlin and Heitor Silverio Lopes",
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title = "A Genetic Programming Approach for Image
Segmentation",
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booktitle = "Computational Intelligence in Image Processing",
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publisher = "Springer",
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year = "2013",
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editor = "Amitava Chatterjee and Patrick Siarry",
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chapter = "4",
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pages = "71--90",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-30620-4",
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DOI = "doi:10.1007/978-3-642-30621-1_4",
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abstract = "This work presents a methodology for using genetic
programming (GP) for image segmentation. The image
segmentation process is seen as a classification
problem where some regions of an image are labelled as
foreground (object of interest) or background. GP uses
a set of terminals and nonterminals, composed by
algebraic operations and convolution filters. A
function fitness is defined as the difference between
the desired segmented image and that obtained by the
application of the mask evolved by GP. A penalty term
is used to decrease the number of nodes of the tree,
minimally affecting the quality of solutions. The
proposed approach was applied to five sets of images,
each one with different features and objects of
interest. Results show that GP was able to evolve
solutions of high quality for the problem. Thanks to
the penalty term of the fitness function, the solutions
found are simple enough to be used and understood by a
human user.",
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affiliation = "Federal Institute of Education, Science and Technology
of Parana, Campus Paranagua, Paranagua, Brazil",
- }
Genetic Programming entries for
Hugo Alberto Perlin
Heitor Silverio Lopes
Citations