Unsupervised Learning for Edge Detection Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Fu:2014:CEC,
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title = "Unsupervised Learning for Edge Detection Using Genetic
Programming",
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author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
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pages = "117--124",
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booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
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year = "2014",
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month = "6-11 " # jul,
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editor = "Carlos A. {Coello Coello}",
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address = "Beijing, China",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, Genetic programming, Evolutionary
Computer Vision",
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DOI = "doi:10.1109/CEC.2014.6900444",
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abstract = "In edge detection, a machine learning algorithm
generally requires training images with their ground
truth or designed outputs to train an edge detector.
Meanwhile the computational cost is heavy for most
supervised learning algorithms in the training stage
when a large set of training images is used. To learn
edge detectors without ground truth and reduce the
computational cost, an unsupervised Genetic Programming
(GP) system is proposed for low-level edge detection. A
new fitness function is developed from the energy
functions in active contours. The proposed GP system
uses single images to evolve GP edge detectors, and
these evolved edge detectors are used to detect edges
on a large set of test images. The results of the
experiments show that the proposed unsupervised
learning GP system can effectively evolve good edge
detectors to quickly detect edges on different natural
images.",
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notes = "WCCI2014",
- }
Genetic Programming entries for
Wenlong Fu
Mark Johnston
Mengjie Zhang
Citations