Learning Robust Feature Descriptor for Image Registration With Genetic Programming
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gp-bibliography.bib Revision:1.8028
- @Article{Wu:2020:ACC,
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author = "Yue Wu and Qingxiu Su and Wenping Ma and
Shaodi Liu and Qiguang Miao",
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title = "Learning Robust Feature Descriptor for Image
Registration With Genetic Programming",
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journal = "IEEE Access",
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year = "2020",
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volume = "8",
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pages = "39389--39402",
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ISSN = "2169-3536",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ACCESS.2020.2968339",
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abstract = "The robustness and accuracy of feature descriptor are
two essential factors in the process of image
registration. Existing feature descriptors can extract
important image features, but it may be difficult to
find enough correct correspondences for sophisticated
images. And these feature descriptors often require
domain expertise and human intervention. The aim of
this paper is to use Genetic Programming (GP) to
automatically evolve feature descriptors which are
adaptive to various images including remote sensing
images and optical images. In this paper, a novel
GP-based method (GPFD) is proposed to extract feature
vectors and evolve image descriptors for image
registration without supervision. The proposed method
designs a set of simple arithmetic operators and
first-order statistics to construct feature descriptors
in order to reduce noise interference. The performance
of the proposed method is evaluated and compared
against five methods including SIFT, SURF, RIFT, GLPM
and GP. These results demonstrate that the feature
descriptors evolved by GPFD are robust to complex
geometric transformation, the illumination difference
and noise.",
-
notes = "Also known as \cite{8964334}",
- }
Genetic Programming entries for
Yue Wu
Qingxiu Su
Wenping Ma
Shaodi Liu
Qiguang Miao
Citations