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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.
We present a novel approach to robustly detect and perceive vehicles in different camera views as part of a cooperative vehicle-infrastructure system (CVIS). Our formulation is designed for arbitrary camera views and makes no assumptions about intrin
Galaxy surveys probe both structure formation and the expansion rate, making them promising avenues for understanding the dark universe. Photometric surveys accurately map the 2D distribution of galaxy positions and shapes in a given redshift range,
We investigate the quality of associations of astronomical sources from multi-wavelength observations using simulated detections that are realistic in terms of their astrometric accuracy, small-scale clustering properties and selection functions. We
Cosmic voids and their corresponding redshift-aggregated projections of mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures leads to tests comparin
We discuss a novel approach to identifying cosmic events in separate and independent observations. In our focus are the true events, such as supernova explosions, that happen once, hence, whose measurements are not repeatable. Their classification an