Scientific Computing and Imaging Institute, University of Utah, 72 S Central Campus Drive, Room 3750, Salt Lake City, UT 84112, USA.
IEEE Trans Vis Comput Graph. 2012 May;18(5):767-82. doi: 10.1109/TVCG.2011.107.
Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincare´ index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and realworld simulation data to demonstrate their utility.
莫尔斯分解为向量场提供了一种数值稳定的拓扑表示,这对于其严格解释至关重要。然而,莫尔斯分解不是唯一的,其粒度直接影响其计算成本。在本文中,我们提出了一种自动细化方案,以分层方式构建给定向量场的莫尔斯连接图(MCG)。我们的框架允许通过流组合图的局部更新以及莫尔斯集之间的连接区域来细化莫尔斯集。计算速度很快,因为最昂贵的计算集中在域的一小部分。此外,本工作允许生成拓扑一致的 MCG 层次结构,这是无法使用全局方法获得的。提取的莫尔斯集的分类是构建 MCG 的关键步骤,而 Poincare 指数在这方面是不够的。我们利用了由沿着流平移的索引对的贝蒂数提供的 Conley 指数的上界,来对莫尔斯集进行分类。该上界对于莫尔斯集分类足够准确,并为自动细化过程提供了支持信息。开发了一种改进的 MCG 可视化技术来合并 Conley 指数。最后,我们将提出的技术应用于一些合成和真实世界的模拟数据,以证明其效用。