Genetic Programming for Edge Detection via Balancing Individual Training Images
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Fu:2012:CECb,
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title = "Genetic Programming for Edge Detection via Balancing
Individual Training Images",
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author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
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pages = "2702--2709",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6252879",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Conflict of
Interest Papers, Classification, clustering, data
analysis and data mining",
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abstract = "Edge detectors trained by a machine learning algorithm
are usually evaluated by the accuracy based on overall
pixels in the training stage, rather than the
information for each training image. However, when the
evaluation for training edge detectors considers the
accuracy of each image, the influence on the final
detectors has not been investigated. In this study, we
employ genetic programming to evolve detectors with new
fitness functions containing the accuracy of training
images. The experimental results show that fitness
functions based on the accuracy of single training
images can balance the accuracies across detection
results, and the fitness function combining the
accuracy of overall pixels with the accuracy of
training images together can improve the detection
performance.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Wenlong Fu
Mark Johnston
Mengjie Zhang
Citations