III: Medium: Collaborative Research: Situated Visual Information Spaces
III: Medium: Collaborative Research: Situated Visual Information Spaces
批准号:
2107328
负责人:
Hanspeter Pfister
金额:
$40.35万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-10-01 至 2024-09-30
中文摘要
该项目的目的是使人们能够在增强现实中有效地可视化有关世界的信息。增强现实技术可能是计算机技术带来的下一个巨大的社会效益,因为它可以将视觉信息嵌入或“定位”到现实世界中。这使得使用智能手机和智能眼镜的人们能够在正确的现实环境中看到周围的数据。然而,与在普通计算机或智能手机显示器上可视化数据不同,设计人员可以完全控制应用程序的外观和感觉,增强现实可视化本质上是覆盖在现实世界上的。因此,可视化必须能够对不同的现实世界环境做出反应,包括动态场景,并且有设计建议,说明可视化应该如何对不同的环境做出反应。该项目将科学地研究增强现实的可视化,研究不同方法的功效,创建设计建议,然后构建一个软件系统,可以应用这些建议来帮助设计和运行有效的可视化应用程序。所提出的方法将在体育和医疗保健领域进行实验验证。定位的视觉信息空间使用增强现实技术将数字信息世界与物体、人、位置和环境的物理世界融合在一起。为了实现这一目标,将解决三个科学和设计挑战:(1)位置可视化,交互和协作,这需要直观的原位数据可视化,自然用户交互的物理和数字接口,以及增强现实中的协作方案。本文将研究动态环境和情景背景下空间和非空间数据的新型情境视觉嵌入方法。这些可视化将自动适应物理环境、数字实体、用户和任务,同时使用感知和认知有效的方法,不会压倒用户。(2)通过约束进行设计,其中软件减少了创建适应现实环境的可视化的复杂性。该软件的目标是可视化设计师,并评估指导方针作为约束,然后平衡这些,为当前环境提供适当的数据和设计建议。(3)定位应用,在医疗保健和体育方面的两个健康应用将与各自领域的专家合作开发和评估。在这些领域中,这些领域涵盖了一系列不同的技术、任务和用户。这些应用程序将有助于定义一个可实现的研究范围,与有动机的利益相关者一起驱动它,并通过用例呈现最佳实践。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The aim of this project is to enable people to effectively visualize information about the world in augmented reality. Augmented reality is potentially the next big social benefit from computer technologies, because it allows visual information to be embedded - or ‘situated’ - into the real world. This allows people using smartphones and smartglasses to see data around them in the correct real-world context. However, unlike when visualizing data on a regular computer or smartphone display, where a designer has complete control over how the application looks and feels, augmented reality visualizations are inherently overlaid on the real world. As such, visualizations must be capable of reacting to different real-world environments including dynamic scenes, and for there to be design recommendations that say how visualizations should react to different environments. This project will scientifically investigate visualization for augmented reality, study the efficacy of different approaches, create design recommendations, and then build a software system that can apply these recommendations to help design and run effective visualization applications. The proposed approach will be experimentally validated in the sports and healthcare domains.Situated visual information spaces fuse the digital information world with the physical world of objects, people, locations, and environments using augmented reality. To realize this, three scientific and design challenges will be tackled: (1) Situated visualization, interaction, and collaboration, which requires intuitive in-situ data visualizations, physical and digital interfaces for natural user interactions, and schemes for collaboration in augmented reality. Novel situated visual embedding methods will be studied for spatial and non-spatial data in dynamic environmental and situational contexts. These visualizations will automatically adapt to the physical environment, digital entities, users, and tasks while using perceptually and cognitively effective methods that do not overwhelm the user. (2) Design via constraints, where software reduces the increased complexity of creating visualizations that adapt to real-world environments. This software is aimed at visualization designers and evaluates guidelines as constraints, then balances these to provide recommendations for appropriate data and designs for the current environment. (3) Situated applications, where two wellness applications in healthcare and sports will be developed and evaluated in partnership with respective domain experts. Within them, these domains cover a spectrum of different techniques, tasks, and users. These applications will help to define an achievable research scope, drive it with motivated stakeholders, and present best-practices via use cases.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.
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Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching
在沉浸式分析中标记视图外的对象以支持情景视觉搜索
DOI:
10.1109/tvcg.2021.3133511
发表时间:
2021
期刊:
IEEE transactions on visualization and computer graphics
影响因子:
5.2
作者:
[Lin, T., Yang, Y., Beyer, J., Pfister, H.]
通讯作者:
Pfister, H.
DOI:
10.1109/tvcg.2022.3209497
发表时间:
2022-09
期刊:
IEEE Transactions on Visualization and Computer Graphics
影响因子:
5.2
作者:
[Zhutian Chen;Qisen Yang;Xiao Xie;Johanna Beyer;Haijun Xia;Yingnian Wu;H. Pfister]
通讯作者:
Zhutian Chen;Qisen Yang;Xiao Xie;Johanna Beyer;Haijun Xia;Yingnian Wu;H. Pfister
DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification
DeepLIIF:用于 IHC 图像量化的深度学习推断多重免疫荧光
DOI:
--
发表时间:
2022
期刊:
Nature machine intelligence
影响因子:
23.8
作者:
[Ghahremani, P., Li, Y., Kaufman, A. E., Vanguri, R., Greenwald, N., Angelo, M., Hollmann, T. J., Nadeem, S.]
通讯作者:
Nadeem, S.
DOI:
10.1109/tvcg.2023.3327161
发表时间:
2024-01-01
期刊:
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
影响因子:
5.2
作者:
[Lin,Tica, Aouididi,Alexandre, Wang,Jui-Hsien]
通讯作者:
Wang,Jui-Hsien
The Ball is in Our Court: Conducting Visualization Research With Sports Experts
球在我们的球场上:与体育专家进行可视化研究
DOI:
10.1109/mcg.2022.3222042
发表时间:
2023
期刊:
IEEE Computer Graphics and Applications
影响因子:
1.8
作者:
[Lin, Tica, Chen, Zhutian, Beyer, Johanna, Wu, Yingcai, Pfister, Hanspeter, Yang, Yalong]
通讯作者:
Yang, Yalong
共 13 条
NCS-FO: Empowering Data-Driven Hypothesis Generation for Scalable Connectomics Analysis
-
批准号:2124179
-
项目类别:Standard Grant
-
资助金额:$100.0万
-
财政年份:2021
-
负责人:Hanspeter Pfister
-
依托单位:
III: Medium: Visually Interactive Neural Probabilistic Models of Language
-
批准号:1901030
-
项目类别:Continuing Grant
-
资助金额:$120.0万
-
财政年份:2019
-
负责人:Hanspeter Pfister
-
依托单位:
NCS-FO: Analyzing Synapses, Motifs and Neural Networks for Large-Scale Connectomics
-
批准号:1835231
-
项目类别:Standard Grant
-
资助金额:$99.96万
-
财政年份:2018
-
负责人:Hanspeter Pfister
-
依托单位:
US-Israel Collaboration: Collaborative Research: New Tools for Extracting Neuronal Phenotypes from a Volumetric Set of Cerebral Cortex Images
-
批准号:1607800
-
项目类别:Standard Grant
-
资助金额:$39.24万
-
财政年份:2016
-
负责人:Hanspeter Pfister
-
依托单位:
BIGDATA: IA: DKA: Collaborative Research: High-Throughput Connectomics
-
批准号:1447344
-
项目类别:Standard Grant
-
资助金额:$93.5万
-
财政年份:2014
-
负责人:Hanspeter Pfister
-
依托单位:
CGV: Large: Collaborative Research: Analyzing Images Through Time
-
批准号:1110955
-
项目类别:Continuing Grant
-
资助金额:$42.37万
-
财政年份:2011
-
负责人:Hanspeter Pfister
-
依托单位:
CGV: Small: Collaborative Research: From Virtual to Real
-
批准号:1116619
-
项目类别:Standard Grant
-
资助金额:$25.0万
-
财政年份:2011
-
负责人:Hanspeter Pfister
-
依托单位:
CDI Type II: Scientific Computation for Astronomy, Neurobiology and Chemistry using Graphics Processing Units and Solid-State Storage
-
批准号:0835713
-
项目类别:Standard Grant
-
资助金额:$199.39万
-
财政年份:2008
-
负责人:Hanspeter Pfister
-
依托单位:
海外基金