Fast Unsupervised Edge Detection Using Genetic Programming [Application Notes]
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Fu:2018:ieeeCIM,
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author = "Wenlong Fu and Bing Xu and Mengjie Zhang and
Mark Johnston",
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journal = "IEEE Computational Intelligence Magazine",
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title = "Fast Unsupervised Edge Detection Using Genetic
Programming [Application Notes]",
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year = "2018",
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volume = "13",
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number = "4",
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pages = "46--58",
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abstract = "Edge detection has been a fundamental and important
task in computer vision for many years, but it is still
a challenging problem in real-time applications,
especially for unsupervised edge detection, where
ground truth is not available. Typical fast edge
detection approaches, such as the single threshold
method, are expensive to achieve in unsupervised edge
detection. This study proposes a Genetic Programming
(GP) based algorithm to quickly and automatically
extract binary edges in an unsupervised manner. We
investigate how GP can effectively evolve an edge
detector from a single image without ground truth, and
whether the evolved edge detector can be directly
applied to other unseen/test images. The proposed
method is examined and compared with a recent GP method
and the Canny method on the Berkeley segmentation
dataset. The results show that the proposed GP method
has the ability to effectively evolve edge detectors by
using only a single image as the whole training set,
and significantly outperforms the two methods it is
compared to. Furthermore, the binary edges detected by
the evolved edge detectors have a good balance between
recall and precision.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/MCI.2018.2866729",
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ISSN = "1556-603X",
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month = nov,
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notes = "Also known as \cite{8492376}",
- }
Genetic Programming entries for
Wenlong Fu
Bing Xu
Mengjie Zhang
Mark Johnston
Citations