论文标题
水平集有限的Voronoi Tessellation用于大规模空间统计分析
Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis
论文作者
论文摘要
多元体积数据的空间统计分析由于尺度，复杂性和遮挡而可能具有挑战性。拓扑细分，特征提取和统计摘要的进步有助于克服挑战。这项工作引入了一种新的空间统计分解方法，基于级别集，连接的组件以及受限的质心Voronoi Tessellation的新颖变化，该变化更适合于空间统计分解和平行效率。所得的数据结构将特征组织到连贯的嵌套层次结构中，以支持柔性和高效的核心外部利益提取。接下来，我们提供有效的并行实现。最后，设计了基于这种方法的交互式可视化系统，然后应用于湍流燃烧数据。该组合方法可以通过多级详细信息与自上而下的方法进行交互式空间统计分析工作流程，以将相位空间统计信息与空间特征联系起来。
Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.
