SGER: Volume Comparisons

SGER：数量比较

• 批准号: 0540653
• 负责人: Michael Gleicher
• 金额: --
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540653&HistoricalAwards=false
• 关键词: SGER; Volume; Comparisons

Volume data is a vital part of many types of scientific and medical inquiry. Volume visualization has provided tools to help scientists examine volume data: methods exist for display of massive datasets and to display moderate datasets interactively using commodity hardware. Synthetic appearance methods to provide fake lighting and stylized (e.g. non-photorealistic) rendering methods help make complex shapes easier to comprehend. However, all of these advances focus on tools to help a scientist examine a single volumetric dataset.There is a need for tools that explicitly help scientists make visual comparisons among volume datasets. For example, a scientist may need to find differences between two specimens or to look for trends in a sequence of volumes. The need for such tools will grow as the throughput of data generation increases: the proliferation of scanning means more specimens will be scanned; noninvasive imaging techniques allow for series of scans over the development of an animal; and grid-based supercomputing allows for large numbers of independent simulations to be run.

体数据是许多类型的科学和医学探究的重要组成部分。体积可视化提供了帮助科学家检查体积数据的工具：存在用于显示大量数据集以及使用商用硬件以交互方式显示中等数据集的方法。提供假照明的合成外观方法和风格化（例如非真实感）渲染方法有助于使复杂的形状更容易理解。然而，所有这些进步都集中在帮助科学家检查单个体积数据集的工具上。需要能够明确帮助科学家在体积数据集之间进行视觉比较的工具。例如，科学家可能需要找出两个样本之间的差异或寻找一系列体积中的趋势。随着数据生成吞吐量的增加，对此类工具的需求也会增长：扫描的激增意味着将扫描更多的标本；无创成像技术可以对动物的发育过程进行一系列扫描；基于网格的超级计算允许运行大量独立的模拟。