A Supervised Figure-ground Segmentation Method using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Liang:2015:evoApplications,
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author = "Yuyu Liang and Mengjie Zhang and Will Browne",
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title = "A Supervised Figure-ground Segmentation Method using
Genetic Programming",
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booktitle = "18th European Conference on the Applications of
Evolutionary Computation",
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year = "2015",
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editor = "Antonio M. Mora and Giovanni Squillero",
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series = "LNCS",
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volume = "9028",
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publisher = "Springer",
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pages = "491--503",
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address = "Copenhagen",
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month = "8-10 " # apr,
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, Image
segmentation, Raw pixel values, Grayscale statistics",
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isbn13 = "978-3-319-16548-6",
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DOI = "doi:10.1007/978-3-319-16549-3_40",
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abstract = "Figure-ground segmentation is an important
preprocessing phase in many computer vision
applications. As different classes of objects require
specific segmentation rules, supervised (or top-down)
methods, which learn from prior knowledge of objects,
are suitable for figure-ground segmentation. However,
existing top-down methods, such as model-based and
fragment-based ones, involve a lot of human work. As
genetic programming (GP) can evolve computer programs
to solve problems automatically, it requires less human
work. Moreover, since GP contains little human bias, it
is possible for GP-evolved methods to obtain better
results than human constructed approaches. This paper
develops a supervised GP-based segmentation system.
Three kinds of simple features, including raw pixel
values, six dimension and eleven dimension grayscale
statistics, are employed to evolve image segmentors.
The evolved segmentors are tested on images from four
databases with increasing difficulty, and results are
compared with four conventional techniques including
thresholding, region growing, clustering, and active
contour models. The results show that GP-evolved
segmentors perform better than the four traditional
methods with consistently good results on both simple
and complex images.",
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notes = "EvoIASP EvoApplications2015 held in conjunction with
EuroGP'2015, EvoCOP2015 and EvoMusArt2015
http://www.evostar.org/2015/cfp_evoapps.php",
- }
Genetic Programming entries for
Yuyu Liang
Mengjie Zhang
Will N Browne
Citations