Automatic learning of image filters using Cartesian genetic programming
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- @Article{journals/icae/ParisPN15,
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author = "P. C. D. Paris and Emerson Carlos Pedrino and
Maria do Carmo Nicoletti",
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title = "Automatic learning of image filters using Cartesian
genetic programming",
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journal = "Integrated Computer-Aided Engineering",
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year = "2015",
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volume = "22",
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number = "2",
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pages = "135--151",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, CGP-based computational model,
mathematical morphology, morphological image filters,
automatic construction of image filters",
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bibdate = "2015-04-24",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/icae/icae22.html#ParisPN15",
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DOI = "doi:10.3233/ICA-150482",
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size = "17 pages",
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abstract = "This paper proposes a computational modelling for
image filtering processes based on the Cartesian
Genetic Programming (CGP) methodology, suitable for
hardware devices. A computational system named ALIF-CGP
(Automatic Learning of Image Filters Using Cartesian
Genetic Programming) was designed as a simulator for
automatically constructing a sequence of operators,
mainly morphological and logical, which can filter a
particular shape of image. ALIF-CGP is a convenient
option for executing the non-trivial task, usually
manually done by human experts, of selecting the
sequence of nonlinear operators to be used in
morphological filters. ALIF-CGP has already a built-in
pool of morphological and logical operators, which can
be used by default. The user, however, has the
flexibility of choosing only those operators which are
of interest or then, conveniently introduce new ones.
The system expects as input a pair of images
(input-target). The flexibility given by the CGP-based
computational modeling used by ALIF-CGP as well as its
efficiency and satisfactory results, obtained in
various image processing case studies, recommend its
use when developing a hardware implementation for the
purposes of image filtering. A few case studies using
ALIF-CGP are presented and comparatively analysed in
relation to previous results available in the
literature.",
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notes = "Matlab",
- }
Genetic Programming entries for
Paulo Cesar Donizeti Paris
Emerson Carlos Pedrino
Maria do Carmo Nicoletti
Citations