大规模空间统计分析的水平集约束Voronoi网格划分
Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis
摘要：
由于规模、复杂性和遮挡，多变量体积数据的空间统计分析可能具有挑战性。拓扑分割、特征提取和统计摘要方面的进展有助于克服这些挑战。这项工作介绍了一种新的空间统计分解方法的基础上水平集，连接组件，和一个新的变化的限制质心Voronoi镶嵌，更适合空间统计分解和并行效率。由此产生的数据结构组织功能到一个连贯的嵌套层次结构，以支持灵活和高效的核心外区域的兴趣提取。接下来，我们提供了一个高效的并行实现。最后，基于该方法设计了一个交互式可视化系统，并将其应用于湍流燃烧数据。该组合方法通过将相空间统计与空间特征相联系的多层次细节，利用自上而下的方法实现了大规模数据的交互式空间统计分析工作流。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
