DisPerSE stands for Discrete Persistent Structures Extractor

and its main purpose is to identify persistent topological features such as peaks, voids, walls and in particular filamentary structures within sampled distributions in 2D, 3D, and possibly more ...

Although it was initially developed with cosmology in mind (for the study of the properties of filamentary structures in the so called comic web of galaxy distribution over large scales in the Universe), the present version is quite versatile and should be useful for any application where a robust structure identification is required, for segmentation or for studying the topology of sampled functions (like computing persistent Betti numbers for instance).

DisPerSE is able to deal directly with noisy datasets using the concept of persistence (a measure of the robustness of topological features) and can work indifferently on many kinds of cell complex (such as structured and unstructured grids, 2D manifolds embedded within a 3D space, discrete point samples using delaunay tesselation, Healpix tesselations of the sphere, ...). The only constraint is that the distribution must be defined over a manifold, possibly with boundaries.