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Multi-Scale Image Registration Algorithm based on Improved SIFT

机译:基于改进SIFT的多尺度图像配准算法

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摘要

This paper proposes an image registration algorithm based on improved SIFT feature. In the feature detection, evenly distributed feature points are detected using non-maximum suppression and reduces detection times by dynamic adjustment of detecting step. While employs feature matching, the threshold of Euclidean distance ratio is adaptively determined according to the properties of images. Lastly, the error matching points are removed by geometric constraint consistency between the feature points through RANSAC algorithm. Experimental results show the effectiveness of the proposed algorithm.
机译:提出了一种基于改进SIFT特征的图像配准算法。在特征检测中,使用非最大抑制来检测均匀分布的特征点,并通过动态调整检测步骤来减少检测时间。当采用特征匹配时,欧氏距离比的阈值根据图像的属性自适应地确定。最后,通过RANSAC算法通过特征点之间的几何约束一致性消除误差匹配点。实验结果表明了该算法的有效性。

著录项

  • 来源
    《Journal of Multimedia》 |2013年第6期|755-761|共7页
  • 作者单位

    Department of Scientific Research, Air Force Early Warning Academy, Wuhan, 430019, Hubei, China;

    Department of Scientific Research, Air Force Early Warning Academy, Wuhan, 430019, Hubei, China;

    Department of Scientific Research, Air Force Early Warning Academy, Wuhan, 430019, Hubei, China;

    Department of Civil Engineering of Urban Construction, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China;

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  • 正文语种 eng
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  • 关键词

    First Term; Second Term; Third Term; Fourth Term; Fifth Term; Sixth Term;

    机译:第一学期;第二期;第三学期;第四学期;第五学期;第六学期;

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