Skeletonisation algorithms with theoretical guarantees for unorganised point clouds with high levels of noise

摘要

Data Science aims to extract meaningful knowledge from unorganised data. Real datasets usually come in the form of a cloud of points. It is a requirement of numerous applications to visualise an overall shape of a noisy cloud of points sampled from a non-linear object that is more complicated than a union of disjoint clusters. The skeletonisation problem in its hardest form is to find a 1-dimensional skeleton that correctly represents the shape of the cloud.