DisPerSE：2D和3D中的鲁棒结构识别
DisPerSE: robust structure identification in 2D and 3D
摘要：
我们提出了离散持久结构提取器（DisPerSE），一个开源软件，用于在2D和3D噪声数据集中自动和鲁棒地识别结构。该软件旨在通过分割来识别各种拓扑结构，如空洞、峰、源、壁和细丝，并特别强调后者。基于离散Morse理论，DisPerSE能够使用持久性（拓扑特征的鲁棒性的度量）的概念直接处理噪声数据集，并且可以无差别地应用于定义在可能有界流形上的各种数据集：2D和3D图像，结构化和非结构化网格，通过Delaunay Tesselation的离散点样本，球体的Healpix Tesselations，...虽然它最初是在考虑宇宙学的情况下开发的，但已经实现了各种I/O格式，并且当前版本非常通用。因此，它应该是有用的任何应用程序，需要一个强大的结构识别，以及为研究采样函数的拓扑结构（例如计算持久贝蒂数）。DisPerSE可以直接从网站http://www2.iap.fr/users/sousbie/下载，也可以在同一地址获得完整的在线文档。
英文摘要：
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.
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