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hopfliking铁杆木虫 (小有名气)
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| A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information |
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muse
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3楼2014-09-21 19:26:50
muse
捐助贵宾 (知名作家)
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【答案】应助回帖
★ ★ ★ ★ ★
感谢参与，应助指数 +1
hopfliking: 金币+5, ★★★★★最佳答案 2014-09-21 19:37:40
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-22 17:56:50
感谢参与，应助指数 +1
hopfliking: 金币+5, ★★★★★最佳答案 2014-09-21 19:37:40
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-22 17:56:50
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A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information 作者:Gong, MG (Gong, Maoguo)[ 1 ] ; Zhao, SM (Zhao, Shengmeng)[ 1 ] ; Jiao, LC (Jiao, Licheng)[ 1 ] ; Tian, DY (Tian, Dayong)[ 1 ] ; Wang, S (Wang, Shuang)[ 1 ] IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷: 52 期: 7 页: 4328-4338 DOI: 10.1109/TGRS.2013.2281391 出版年: JUL 2014 查看期刊信息 摘要 Automatic image registration is a vital yet challenging task, particularly for remote sensing images. A fully automatic registration approach which is accurate, robust, and fast is required. For this purpose, a novel coarse-to-fine scheme for automatic image registration is proposed in this paper. This scheme consists of a preregistration process (coarse registration) and a fine-tuning process (fine registration). To begin with, the preregistration process is implemented by the scale-invariant feature transform approach equipped with a reliable outlier removal procedure. The coarse results provide a near-optimal initial solution for the optimizer in the fine-tuning process. Next, the fine-tuning process is implemented by the maximization of mutual information using a modified Marquardt-Levenberg search strategy in a multiresolution framework. The proposed algorithm is tested on various remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal) with the affine transformation model. The experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm. 关键词 作者关键词:Image registration; mutual information (MI); outlier removal; scale-invariant feature transform (SIFT) KeyWords Plus:SEGMENTATION; OPTIMIZATION 作者信息 通讯作者地址: Gong, MG (通讯作者) [显示增强组织信息的名称] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China. 地址: [显示增强组织信息的名称] [ 1 ] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China 电子邮件地址:gong@ieee.org 基金资助致谢 基金资助机构 授权号 National Natural Science Foundation of China 61273317 National Top Youth Talents Support Program of China Fundamental Research Fund for the Central Universities K50510020001 K5051202053 查看基金资助信息 出版商 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 类别 / 分类 研究方向:Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology Web of Science 类别:Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology 文献信息 文献类型:Article 语种:English 入藏号: WOS:000332597100050 ISSN: 0196-2892 电子 ISSN: 1558-0644 期刊信息 目录： Current Contents Connect® Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: AC5YO Web of Science 核心合集中的 "引用的参考文献": 39 Web of Science 核心合集中的 "被引频次": 0 |
2楼2014-09-21 19:26:30
baiyuefei
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【答案】应助回帖
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感谢参与，应助指数 +1
sunshan4379: 金币+2, 感谢应助！ 2014-09-22 17:57:03
感谢参与，应助指数 +1
sunshan4379: 金币+2, 感谢应助！ 2014-09-22 17:57:03
4楼2014-09-21 19:26:58
