Low-Level Feature Extraction for Edge Detection Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Fu:2014:ieeec,
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author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
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title = "Low-Level Feature Extraction for Edge Detection Using
Genetic Programming",
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journal = "IEEE Transactions on Cybernetics",
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year = "2014",
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volume = "44",
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number = "8",
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month = "1459--1472",
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keywords = "genetic algorithms, genetic programming, Accuracy,
Detectors, Educational institutions, Feature
extraction, Image edge detection, Noise, Training, Edge
detection, feature extraction",
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ISSN = "2168-2267",
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DOI = "doi:10.1109/TCYB.2013.2286611",
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size = "14 pages",
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abstract = "Edge detection is a subjective task. Traditionally, a
moving window approach is used, but the window size in
edge detection is a tradeoff between localisation
accuracy and noise rejection. An automatic technique
for searching a discriminated pixel's neighbours to
construct new edge detectors is appealing to satisfy
different tasks. In this paper, we propose a genetic
programming (GP) system to automatically search pixels
(a discriminated pixel and its neighbours) to construct
new low-level subjective edge detectors for detecting
edges in natural images, and analyse the pixels
selected by the GP edge detectors. Automatically
searching pixels avoids the problem of blurring edges
from a large window and noise influence from a small
window. Linear and second-order filters are constructed
from the pixels with high occurrences in these GP edge
detectors. The experiment results show that the
proposed GP system has good performance. A comparison
between the filters with the pixels selected by GP and
all pixels in a fixed window indicates that the set of
pixels selected by GP is compact but sufficiently rich
to construct good edge detectors.",
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notes = "also known as \cite{6649981}",
- }
Genetic Programming entries for
Wenlong Fu
Mark Johnston
Mengjie Zhang
Citations