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An effective histogram binning for mutual information based registration of optical imagery and 3D LiDAR data

机译:有效的直方图分类,用于基于互信息的光学图像和3D LiDAR数据配准

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Automatic registration of multi-sensor data is a basic step in data fusion applications. Mutual information (MI) has been widely used in medical and remote sensing image registration. In this paper, an effective histogram binning technique is proposed to improve the robustness of image registration using MI and Normalized MI (NMI). Increasing the bin size improves the robustness of MI to local maxima that occur in the convergence surface of MI. In addition, the computation cost of registration is decreased due to use of a smaller joint pdf, without decreasing the accuracy. The performance of the proposed method in the registration of aerial imagery with LiDAR data has been experimentally evaluated and the results obtained are presented.
机译:自动注册多传感器数据是数据融合应用程序中的基本步骤。互信息(MI)已被广泛用于医学和遥感图像配准。本文提出了一种有效的直方图合并技术,以提高使用MI和Normalized MI(NMI)进行图像配准的鲁棒性。增大容器大小可提高MI对出现在MI会聚表面上的局部最大值的鲁棒性。另外,由于使用较小的联合pdf,减少了注册的计算成本,而没有降低准确性。实验评估了该方法在航空影像与LiDAR数据配准中的性能,并给出了获得的结果。

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