Implicit Fitness Sharing for Evolutionary Synthesis of License Plate Detectors
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Krawiec:2013:EvoIASP,
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author = "Krzysztof Krawiec and Mateusz Nawrocki",
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title = "Implicit Fitness Sharing for Evolutionary Synthesis of
License Plate Detectors",
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booktitle = "Applications of Evolutionary Computing,
EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
EvoRISK, EvoROBOT, EvoSTOC",
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year = "2013",
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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",
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volume = "7835",
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series = "Lecture Notes in Computer Science",
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pages = "376--386",
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address = "Vienna, Austria",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, pattern
recognition, image analysis, implicit fitness sharing,
car number plate recognition",
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isbn13 = "978-3-642-37191-2",
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DOI = "doi:10.1007/978-3-642-37192-9_38",
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size = "11 pages",
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abstract = "A genetic programming algorithm for synthesis of
object detection systems is proposed and applied to the
task of license plate recognition in uncontrolled
lighting conditions. The method evolves solutions
represented as data flows of high-level parametric
image operators. In an extended variant, the algorithm
employs implicit fitness sharing, which allows
identifying the particularly difficult training
examples and focusing the training process on them. The
experiment, involving heterogeneous video sequences
acquired in diverse conditions, demonstrates that
implicit fitness sharing substantially improves the
predictive performance of evolved detection systems,
providing maximum recognition accuracy achievable for
the considered setup and training data.",
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notes = "
EvoApplications2013 held in conjunction with
EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
- }
Genetic Programming entries for
Krzysztof Krawiec
Mateusz Nawrocki
Citations