GeodisTK：​​用于2D和3D图像的测地距离转换工具包 可以通过两种方法来实现图像的测地变换：快速行进和光栅扫描。 快速行进基于速度为F [1]的像素前沿的迭代传播。 光栅扫描基于内核操作，这些内核操作在多遍处理中依次应用于图像[2] [3]。 在GeoS [4]中，作者建议使用3x3内核进行正向和反向传递，以进行有效的测地距离转换，该转换用于图像分割。 栅格扫描以进行测地距离转换。 图片来自[4]。 DeepIGeoS [5]提出将测地距离变换与卷积神经网络相结合，以有效地对2D和3D图像进行交互式分割。 [1] Sethian，James A.“快速行进方法”。 SIAM评论41，没有。 2（1999）：199-235。 [2] Borgefors，古尼拉。 “数字图像中的距离转换。” CVPR，1986年 [3] Toivanen，PekkaJ。“用于灰度图像的新测地距

matlab开发-两点间的测地距离。求图像上两点间的最小测地线距离

基于测地距离的谱聚类，利用测地距离代替欧式聚类，代码以同心圆为样本数据进行实例演示！

这是一篇来自science杂志的论文，非常经典！介绍了测地距离在流行降维中的应用。 Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: Þnding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputsÑ30,000 auditory nerve Þbers or 106 optic nerve ÞbersÑa manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efÞciently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

计算测地距离的matlab代码，包括Dijkstra和Floyd 两种方法的代码，以及一个瑞士卷的例子