AF: Small: Collaborative Research: Reeb graph flows: Metrics, Drawings, and Analysis

AF：小型：协作研究：Reeb 图流：指标、绘图和分析

• 批准号: 1907591
• 负责人: Elizabeth Munch
• 金额: $ 24.66万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907591&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Reeb

Shape data such as images and 3d scans come from a wide variety of sources, including medical imaging, biological analysis of plants and animals, digital preservation of artifacts, computer graphics and animation, and many others. To distill this data to its essence, this project uses tools from computational topology, a field which combines tools from mathematics and computer science to find efficient and practical ways to simplify, store, and analyze these images. One of these tools, the Reeb graph, provides a skeleton of the structure which is useful for visualization. This project creates tools to quantify, visualize, and analyze these skeletons in a time-varying fashion, i.e. when the input image data is actually a movie. Given how often multiple images or scans are collected from an object in motion or over time, the results of this project have broad applications in many fields. These results will be disseminated through publications, presentations, open-source software, and participation in a variety of workshops and activities, all of which continue collaborative and interdisciplinary work in a larger network of shape-analysis and computational-topology researchers. In addition to core theoretical development of new tools, both PIs are dedicated to broadening participation in mathematics and computer science, and as part of the project will focus extensively on teaching and mentoring students and junior researchers, including active leadership and mentoring in societies that focus primarily on underrepresented groups.In numerous application fields, there is an increasing need to analyze topological and geometric information about shapes that are generated by scanning some 3d structure. However, when given a time-varying shape, there are few strategies that utilize prior scans to quickly update and maintain a continuous family of visualizations with desirable properties. In this project, the PIs will collaborate to develop improved visualization toolkits for one of the major ways to analyze this type of data, the Reeb graph, which is heavily used in computational topology, shape analysis, and visualization. These skeletons give compressed and accurate ways to store the shape and compute shape invariants and statistics, but can nonetheless be large and difficult to visualize. In particular, the project is focused on the analysis of a time-varying Reeb graph, known as a Reeb graph flow. Over the course of the project, the PIs will investigate properties of such Reeb flows coming from a variety of natural input domains, and determine which settings allow faster and more accurate visualization and analysis. Results from this project will include a range of theoretical and applied publications, open-source software development, and will be disseminated to a larger network of researchers interested in shape analysis and the use of topology for algorithms and analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

诸如图像和3d扫描之类的形状数据来自各种各样的来源，包括医学成像、植物和动物的生物分析、人工制品的数字保存、计算机图形和动画以及许多其他来源。为了提取这些数据的本质，该项目使用了计算拓扑学的工具，该领域结合了数学和计算机科学的工具，以找到有效和实用的方法来简化，存储和分析这些图像。这些工具之一，Reeb图，提供了一个骨架的结构，这是有用的可视化。该项目创建了一些工具，用于以随时间变化的方式量化、可视化和分析这些骨架，即当输入的图像数据实际上是一部电影时。 考虑到从运动中的物体或随着时间的推移收集多个图像或扫描的频率，该项目的结果在许多领域都有广泛的应用。这些成果将通过出版物、演讲、开源软件以及参加各种研讨会和活动来传播，所有这些都将继续在形状分析和计算拓扑研究人员的更大网络中开展协作和跨学科工作。除了新工具的核心理论开发之外，两个PI都致力于扩大数学和计算机科学的参与，并且作为项目的一部分，将广泛关注教学和指导学生和初级研究人员，包括主要关注代表性不足群体的社会中的积极领导和指导。在许多应用领域，越来越需要分析关于通过扫描某些3D结构而生成的形状的拓扑和几何信息。 然而，当给定随时间变化的形状时，很少有策略利用先前的扫描来快速更新和维护具有期望属性的连续可视化家族。 在这个项目中，PI将合作开发改进的可视化工具包，用于分析这类数据的主要方法之一，Reeb图，它在计算拓扑学，形状分析和可视化中得到了广泛的应用。 这些骨架提供了压缩和准确的方式来存储形状和计算形状不变量和统计数据，但仍然很大并且难以可视化。特别是，该项目专注于分析时变Reeb图，称为Reeb图流。在项目过程中，PI将调查来自各种自然输入域的Reeb流的属性，并确定哪些设置可以更快，更准确地进行可视化和分析。该项目的成果将包括一系列理论和应用出版物、开源软件开发，并将传播给对形状分析和使用拓扑算法和分析感兴趣的更大的研究人员网络。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

A family of metrics from the truncated smoothing of Reeb graphs

• DOI: 10.4230/lipics.socg.2021.22
• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: E. Chambers;E. Munch;Tim Ophelders

The shape of aroma: Measuring and modeling citrus oil gland distribution

香气的形状：柑橘油腺分布的测量和建模

• DOI: 10.1002/ppp3.10333
• 发表时间: 2022
• 期刊: PLANET
• 作者: Amézquita, Erik J.;Quigley, Michelle Y.;Ophelders, Tim;Seymour, Danelle;Munch, Elizabeth;Chitwood, Daniel H.
• 通讯作者: Chitwood, Daniel H.

Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform

• DOI: 10.1093/insilicoplants/diab033
• 发表时间: 2022-01-01
• 期刊: IN SILICO PLANTS
• 影响因子: 3.1
• 作者: Amezquita, Erik J.;Quigley, Michelle Y.;Chitwood, Daniel H.
• 通讯作者: Chitwood, Daniel H.

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

• DOI: 10.1109/icmla.2019.00200
• 发表时间: 2019-10
• 期刊: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
• 作者: Melih C. Yesilli;Sarah Tymochko;Firas A. Khasawneh;E. Munch

Comparing embedded graphs using average branching distance

使用平均分支距离比较嵌入图

• DOI: 10.2140/involve.2023.16.365
• 发表时间: 2023
• 期刊: a Journal of Mathematics
• 作者: Batakci, Levent;Branson, Abigail;Castillo, Bryan;Todd, Candace;Chambers, Erin Wolf;Munch, Elizabeth
• 通讯作者: Munch, Elizabeth