Establishing Hyper-Renormalization for Geometric Estimation from Images

最大似然法已被用于从图像数据中进行高精度的几何估计，如拟合直线和椭圆，以及计算多幅图像上对应点之间的关系，我们推导出了一种更高精度的方法，称为超重整化方法，并将其应用于各种实际问题。同时，推广了修正极大似然解的“超精校正法”，并证实可以达到相同的精度。我们还介绍了扩展FNS方法的一种新公式，并将其应用于分析日本东部大地震的GPS地形变数据。

While maximum likelihood has been used for high accuracy geometric estimation from image data, such as fitting lines and ellipses and computing the relationships between corresponding points over multiple images, we have derived a higher accuracy method, called “hyper-renormalization” and applied it to various practical problems. At the same time, we have extend the “hyper accurate correction” for correcting the maximum likelihood solution and confirmed that the same degree of accuracy can be reached. We have also introduced a new formulation of the extended FNS method and applied it to the analysis of the GPS land deformation data of the Great East Japan Earthquake.