Learning Robust Feature Descriptor for Image Registration With Genetic Programming

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@Article{Wu:2020:ACC,

• author = "Yue Wu and Qingxiu Su and Wenping Ma and Shaodi Liu and Qiguang Miao",
• title = "Learning Robust Feature Descriptor for Image Registration With Genetic Programming",
• journal = "IEEE Access",
• year = "2020",
• volume = "8",
• pages = "39389--39402",
• ISSN = "2169-3536",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ACCESS.2020.2968339",
• 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