Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Magnusson:2016:GECCO,
  author =       "Lars Vidar Magnusson and Roland Olsson",
  title =        "Improving the Canny Edge Detector Using Automatic
                 Programming: Improving Non-Max Suppression",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  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",
  pages =        "461--468",
  keywords =     "genetic algorithms, genetic programming, ADATE",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908926",
  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.",
  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