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Dense matching for wide baseline images based on equal proportion of triangulation

机译:基于等比例的三角剖分的宽基线图像的密集匹配

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摘要

The dense matching of pairs is the basis of advanced image processing applications, such as 3D reconstruction and simultaneous localisation and mapping (SLAM). However, it is difficult to obtain efficient dense matching results in wide baseline image pairs due to various conditions. For this reason, the authors propose a dense matching method for wide baseline images based on an equal proportion of triangulation. Firstly, this Letter proves the mathematical property of equal proportion of triangulation affine transformation, and then builds a triangulation based on the set of sparse matching points. By using this property, the positions of equal proportion points in the corresponding triangulation of the two images to be matched are calculated, respectively. Secondly, to eliminate the points and obtain the inner point set, these proportional points are used to check the similarity of the matching triangle regions. Finally, the inner point set is used to calculate the positions of the dense matching points as the final dense matching result. The experimental results verified the validity and accuracy of the proposed method and this method is suitable for tilt photography, multispectral and multidate remote sensing image dense registration, and more.
机译:对的密集匹配是高级图像处理应用程序的基础,例如3D重建以及同时定位和映射(SLAM)。但是,由于各种条件,很难在宽基线图像对中获得有效的密集匹配结果。因此,作者提出了一种基于等比例的三角剖分的宽基线图像的密集匹配方法。该信首先证明等分的三角仿射变换的数学性质,然后基于稀疏匹配点集建立三角剖分。通过使用该属性,分别计算要匹配的两个图像的相应三角剖分中等比例点的位置。其次,为了消除这些点并获得内部点集,这些比例点用于检查匹配三角形区域的相似性。最后,使用内部点集计算密集匹配点的位置,作为最终的密集匹配结果。实验结果证明了该方法的有效性和准确性,该方法适用于倾斜摄影,多光谱和多日期遥感影像密集配准等。

著录项

  • 来源
    《Electronics Letters》 |2019年第7期|380-382|共3页
  • 作者

    Jia Di; Wu Si; Zhao Mingyuan;

  • 作者单位

    Liaoning Tech Univ, Sch Elect & Informat Engn, Fuxing 125105, Liaoning, Peoples R China;

    Liaoning Tech Univ, Sch Elect & Informat Engn, Fuxing 125105, Liaoning, Peoples R China;

    Liaoning Tech Univ, Sch Elect & Informat Engn, Fuxing 125105, Liaoning, Peoples R China;

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