Reconfigurable architecture for mathematical morphology using genetic programming and FPGAs
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- @InProceedings{Pedrino:2010:IPDPSW,
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author = "Emerson Carlos Pedrino and Osmar Ogashawara and
Valentin Obac Roda",
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title = "Reconfigurable architecture for mathematical
morphology using genetic programming and FPGAs",
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booktitle = "IEEE International Symposium on Parallel Distributed
Processing, Workshops and Phd Forum (IPDPSW), 2010",
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year = "2010",
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month = "19-23 " # apr,
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abstract = "The task of designing manually morphological operators
for a given application is not always a trivial one.
Genetic programming is a branch of evolutionary
computing and it is consolidating as a promising method
for applications of digital image processing. The main
objective of genetic programming is to discover how
computers can learn to solve problems without being
programmed for that. In the literature little has been
found about the automatic morphological construction of
operators using genetic programming. In this paper,
it's presented an original architecture implemented in
a FPGA for classical mathematical morphological (binary
and gray level) operations that are generated
automatically by a genetic programming approach. The
possible applications for the system are: pattern
recognition and emulation of simple filters, to name
just a few. Practical examples using the developed
system are presented.",
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keywords = "genetic algorithms, genetic programming, FPGA, digital
image processing, evolutionary computing, mathematical
morphology, morphological operators, pattern
recognition, reconfigurable architecture, field
programmable gate arrays, image processing,
mathematical morphology, reconfigurable architectures",
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DOI = "doi:10.1109/IPDPSW.2010.5470739",
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notes = "Also known as \cite{5470739}",
- }
Genetic Programming entries for
Emerson Carlos Pedrino
Osmar Ogashawara
Valentin Obac Roda
Citations