Xuan Huang, Haichao Miao, Hyojin Kim, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, Peer-Timo Bremer
{"title":"Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data","authors":"Xuan Huang, Haichao Miao, Hyojin Kim, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, Peer-Timo Bremer","doi":"arxiv-2408.11957","DOIUrl":null,"url":null,"abstract":"Advanced manufacturing creates increasingly complex objects with material\ncompositions that are often difficult to characterize by a single modality. Our\ncollaborating domain scientists are going beyond traditional methods by\nemploying both X-ray and neutron computed tomography to obtain complementary\nrepresentations expected to better resolve material boundaries. However, the\nuse of two modalities creates its own challenges for visualization, requiring\neither complex adjustments of bimodal transfer functions or the need for\nmultiple views. Together with experts in nondestructive evaluation, we designed\na novel interactive bimodal visualization approach to create a combined view of\nthe co-registered X-ray and neutron acquisitions of industrial objects. Using\nan automatic topological segmentation of the bivariate histogram of X-ray and\nneutron values as a starting point, the system provides a simple yet effective\ninterface to easily create, explore, and adjust a bimodal visualization. We\npropose a widget with simple brushing interactions that enables the user to\nquickly correct the segmented histogram results. Our semiautomated system\nenables domain experts to intuitively explore large bimodal datasets without\nthe need for either advanced segmentation algorithms or knowledge of\nvisualization techniques. We demonstrate our approach using synthetic examp","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"434 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.11957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Advanced manufacturing creates increasingly complex objects with material
compositions that are often difficult to characterize by a single modality. Our
collaborating domain scientists are going beyond traditional methods by
employing both X-ray and neutron computed tomography to obtain complementary
representations expected to better resolve material boundaries. However, the
use of two modalities creates its own challenges for visualization, requiring
either complex adjustments of bimodal transfer functions or the need for
multiple views. Together with experts in nondestructive evaluation, we designed
a novel interactive bimodal visualization approach to create a combined view of
the co-registered X-ray and neutron acquisitions of industrial objects. Using
an automatic topological segmentation of the bivariate histogram of X-ray and
neutron values as a starting point, the system provides a simple yet effective
interface to easily create, explore, and adjust a bimodal visualization. We
propose a widget with simple brushing interactions that enables the user to
quickly correct the segmented histogram results. Our semiautomated system
enables domain experts to intuitively explore large bimodal datasets without
the need for either advanced segmentation algorithms or knowledge of
visualization techniques. We demonstrate our approach using synthetic examp
先进的制造业制造出越来越复杂的物体,其材料构成往往难以用单一模式来表征。我们合作的领域科学家正在超越传统方法,同时采用 X 射线和中子计算机断层扫描技术来获得互补的显示结果,从而更好地解析材料边界。然而,两种模式的使用给可视化带来了挑战,需要对双模传递函数进行复杂的调整,或者需要形成多个视图。我们与无损评估专家一起设计了一种新颖的交互式双模可视化方法,以创建工业物体的共聚合 X 射线和中子采集图像的组合视图。该系统以 X 射线和中子值的双变量直方图的自动拓扑分割为起点,提供了一个简单而有效的界面,可轻松创建、探索和调整双模态可视化。我们提出了一个具有简单刷洗交互功能的小工具,使用户能够快速修正分割后的直方图结果。我们的半自动系统能让领域专家直观地探索大型双峰数据集,而无需高级分割算法或可视化技术知识。我们使用合成示例来演示我们的方法。