  We present the DIScrete PERsistent Structures Extractor (DisPerSE), an open
source software for the automatic and robust identification of structures in 2D
and 3D noisy data sets. The software is designed to identify all sorts of
topological structures, such as voids, peaks, sources, walls and filaments
through segmentation, with a special emphasis put on the later ones. Based on
discrete Morse theory, DisPerSE is able to deal directly with noisy datasets
using the concept of persistence (a measure of the robustness of topological
features) and can be applied indifferently to various sorts of data-sets
defined over a possibly bounded manifold : 2D and 3D images, structured and
unstructured grids, discrete point samples via the delaunay tesselation,
Healpix tesselations of the sphere, ...
  Although it was initially developed with cosmology in mind, various I/O
formats have been implemented and the current version is quite versatile. It
should therefore be useful for any application where a robust structure
identification is required as well as for studying the topology of sampled
functions (e.g. computing persistent Betti numbers).
  DisPerSE can be downloaded directly from the website
http://www2.iap.fr/users/sousbie/ and a thorough online documentation is also
available at the same address.
