Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Magnusson:2016:GECCO,
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author = "Lars Vidar Magnusson and Roland Olsson",
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title = "Improving the Canny Edge Detector Using Automatic
Programming: Improving Non-Max Suppression",
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booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2016",
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editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
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pages = "461--468",
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keywords = "genetic algorithms, genetic programming, ADATE",
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month = "20-24 " # jul,
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organisation = "SIGEVO",
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address = "Denver, USA",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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isbn13 = "978-1-4503-4206-3",
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DOI = "doi:10.1145/2908812.2908926",
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abstract = "we employ automatic programming, a relatively unknown
evolutionary computation strategy, to improve the
non-max suppression step in the popular Canny edge
detector. The new version of the algorithm has been
tested on a dataset widely used to benchmark edge
detection algorithms. The performance has increased by
1.9percent, and a pairwise student-t comparison with
the original algorithm gives a p-value of 6.45 x 10-9.
We show that the changes to the algorithm have made it
better at detecting weak edges, without increasing the
computational complexity or changing the overall
design. Previous attempts have been made to improve the
filter stage of the Canny algorithm using evolutionary
computation, but, to our knowledge, this is the first
time it has been used to improve the non-max
suppression algorithm.
The fact that we have found a heuristic improvement to
the algorithm with significantly better performance on
a dedicated test set of natural images suggests that
our method should be used as a standard part of image
analysis platforms, and that our methodology could be
used to improve the performance of image analysis
algorithms in general.",
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notes = "GECCO-2016 A Recombination of the 25th International
Conference on Genetic Algorithms (ICGA-2016) and the
21st Annual Genetic Programming Conference (GP-2016)",
- }
Genetic Programming entries for
Lars Vidar Magnusson
J Roland Olsson
Citations