Heat method of non-uniform diffusion for computing geodesic distance on images and surfaces

This paper presents a non-uniform heat method to calculate geodesic distance and geodesic curves on the images and surfaces. Different from the varadhan’s formula-based heat method, our non-uniform heat method first finds the direction of distance increases by heat diffusion, and then recovers the geodesic distance by solving a Poisson equation. Various heat diffusion metrics obtained from different potentials and tensors, such as intensity-based metrics, gradient-based metrics, and anisotropy metrics et., describe the differences of geodesic distances in various regions. Combined with automatic geodesic segmentation technology, our heat method can be effectively and quickly applied to centerlines extraction and salient curves detection in images, skeleton extraction of shapes, and 3D path planning on surfaces. Two categories of discretization algorithms on scattered points and triangle meshes are more flexible and can often be used to more complicated cases. The algorithm is robust and simple to implement since it is based on solving a pair of standard sparse linear systems. Pre-calculation also greatly reduces time consumption and memory footprint.