- author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
- title = "Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming",
- booktitle = "Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC",
- year = "2013",
- month = "3-5 " # apr,
- editor = "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe {De Falco} and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco {Fernandez de Vega} and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen",
- series = "LNCS",
- volume = "7835",
- publisher = "Springer Verlag",
- address = "Vienna",
- publisher_address = "Berlin",
- pages = "365--375",
- organisation = "EvoStar",
- keywords = "genetic algorithms, genetic programming, Edge Detection, Gaussian Filter",
- isbn13 = "978-3-642-37191-2",
-
DOI = "
doi:10.1007/978-3-642-37192-9_37",
- size = "11 pages",
- abstract = "Gaussian-based edge detectors have been developed for many years, but there are still problems with how to set scales for Gaussian filters and how to combine Gaussian filters. In order to address both problems, a Genetic Programming (GP) system is proposed to automatically choose scales for Gaussian filters and automatically combine Gaussian filters. In this study, the GP system is used to construct rotation invariant Gaussian-based edge detectors based on a benchmark image dataset. The experimental results show that the GP evolved Gaussian-based edge detectors are better than the Gaussian gradient and rotation invariant surround suppression to extract edge features.",
-
notes = "
EvoApplications2013 held in conjunction with EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
