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A Regenerated Feature Extraction Method for Cross-modal Image Registration
Yang, Jian1; Wang, Qi1,2; Li, Xuelong3
2018
Conference Name9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
Source PublicationAdvances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings
Volume10989 LNAI
Pages441-451
Conference Date2018-07-07
Conference PlaceXi'an, China
PublisherSpringer Verlag
Contribution Rank3
AbstractCross-modal image registration is an intractable problem in computer vision and pattern recognition. Inspired by that human gradually deepen to learn in the cognitive process, we present a novel method to automatically register images with different modes in this paper. Unlike most existing registrations that align images by single type of features or directly using multiple features, we employ the “regenerated” mechanism cooperated with a dynamic routing to adaptively detect features and match for different modal images. The geometry-based maximally stable extremal regions (MSER) are first implemented to fast detect non-overlapping regions as the primitive of feature regeneration, which are used to generate novel control-points using salient image disks (SIDs) operator embedded by a sub-pixel iteration. Then a dynamic routing is proposed to select suitable features and match images. Experimental results on optical and multi-sensor images show that our method has a better accuracy compared to state-of-the-art approaches. © 2018, Springer Nature Switzerland AG.
Department光学影像学习与分析中心
DOI10.1007/978-3-030-00563-4_43
Indexed ByEI
ISBN9783030005627
Language英语
ISSN03029743;16113349
EI Accession Number20184305979099
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30687
Collection光学影像学习与分析中心
Corresponding AuthorWang, Qi
Affiliation1.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an; Shaanxi; 710072, China;
2.Unmanned System Research Institute (USRI), Northwestern Polytechnical University, Xi’an; Shaanxi; 710072, China;
3.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Science, Xi’an; Shaanxi; 710119, China
Recommended Citation
GB/T 7714
Yang, Jian,Wang, Qi,Li, Xuelong. A Regenerated Feature Extraction Method for Cross-modal Image Registration[C]:Springer Verlag,2018:441-451.
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