The geodesic distance (path length) and effective resistance are both metrics defined on the vertices of a graph. The effective resistance is a more refined measure of connectivity than the geodesic distance. For example if there are k edge disjoint paths of geodesic distance d between two vertices, then the effective resistance is no more than d k . Thus, the more paths, the closer the vertice...

Shape deformations preserving the intrinsic properties of a surface are called isometries. An isometry deforms a surface without tearing or stretching it, and preserves geodesic distances. We present a technique for matching point set surfaces, which is invariant with respect to isometries. A set of reference points, evenly distributed on the point set surface, is sampled by farthest point samp...

In this paper, a geodesic α-invex subset of a Riemannian manifold is introduced. Geodesic α-invex and α-preinvex functions on a geodesic α-invex set with respect to particular maps are also defined. Further, we study the relationships between geodesic α-invex and α-preinvex functions on Riemannian manifolds. Some results of a non smooth geodesic α-preinvex function are also discussed using prox...

We present the first algorithm to compute the geodesic Fréchet distance between two polygonal curves in a plane with polygonal obstacles, where distances between points are measured as the length of a shortest path between them. Using shortest path structures that we call dynamic and static spotlights, we efficiently construct and propagate reachability information through the free space diagra...

We propose a new image compression method based on geodesic Delaunay triangulations. Triangulations are generated by a progressive geodesic meshing algorithm which exploits the anisotropy of images through a farthest point sampling strategy. This seeding is performed according to anisotropic geodesic distances which force the anisotropic Delaunay triangles to follow the geometry of the image. G...

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest ...

For manifold learning, it is assumed that high-dimensional sample/data points are on an embedded low-dimensional manifold. Usually, distances among samples are computed to represent the underlying data structure, for a specified distance measure such as the Euclidean distance or geodesic distance. For manifold learning, here we propose a metric according to the angular change along a geodesic l...

The space of all probability measures having positive density function on a connected compact smooth manifold $M$, denoted by $\mathcal{P}(M)$, carries the Fisher information metric $G$. We define geometric mean aid which we investigate geometry equipped with show that geodesic segment joining arbitrary $\mu_1$ and $\mu_2$ is expressed using normalized its endpoints. As an application, any two ...

This paper reviews both the theory and practice of the numerical computation of geodesic distances on Riemannian manifolds . The notion of Riemannian manifold allows one to define a local metric (a symmetric positive tensor field) that encodes the information about the problem one wishes to solve. This takes into account a local isotropic cost (whether some point should be avoided or not) and a...