III: Medium: Collaborative Research: Situated Visual Information Spaces

III：媒介：协作研究：情境视觉信息空间

• 批准号: 2107409
• 负责人: James Tompkin
• 金额: $ 39.65万
• 依托单位: Brown University
• 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
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107409&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Situated

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.

该项目的目的是使人们能够在增强现实中有效地可视化世界信息。增强现实可能是计算机技术带来的下一个重大社会效益，因为它允许视觉信息嵌入或“定位”到真实的世界中。这使得使用智能手机和智能眼镜的人能够在正确的现实环境中查看周围的数据。然而，与在常规计算机或智能手机显示器上可视化数据时不同，在常规计算机或智能手机显示器上，设计师完全控制应用程序的外观和感觉，增强现实可视化本质上覆盖在真实的世界上。因此，可视化必须能够对包括动态场景在内的不同现实世界环境做出反应，并且有设计建议说明可视化应该如何对不同环境做出反应。该项目将科学地调查增强现实的可视化，研究不同方法的有效性，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领域进行实验验证。情境视觉信息空间融合数字信息世界与物理世界的对象，人，位置和环境使用增强现实。为了实现这一目标，将解决三个科学和设计挑战：（1）现场可视化，交互和协作，这需要直观的现场数据可视化，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。新的情境视觉嵌入方法将研究空间和非空间数据在动态环境和情境背景下。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上有效的方法，而不会压倒用户。(2)通过约束进行设计，其中软件降低了创建适应真实世界环境的可视化的复杂性。该软件针对可视化设计人员，并将准则作为约束进行评估，然后平衡这些准则，为当前环境的适当数据和设计提供建议。(3)情境应用，其中医疗保健和体育领域的两个健康应用将与各自的领域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，与积极的利益相关者一起推动它，并通过用例展示最佳实践。该奖项反映了NSF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。

项目成果

How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots

• DOI: 10.1145/3544548.3581111
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Fumeng Yang;Yuxin Ma;Lane Harrison;J. Tompkin;D. Laidlaw

On Human-like Biases in Convolutional Neural Networks for the Perception of Slant from Texture

• DOI: 10.1145/3613451
• 发表时间: 2023-08
• 期刊: ACM Transactions on Applied Perception
• 影响因子: 1.6
• 作者: Yuanhao Wang;Qian Zhang;Celine Aubuchon;Jovan T. Kemp;F. Domini;J. Tompkin

Dually Noted: Layout-Aware Annotations with Smartphone Augmented Reality

双重注释：通过智能手机增强现实进行布局感知注释

• DOI: 10.1145/3491102.3502026
• 发表时间: 2022
• 期刊: ACM CHI Conference on Human Factors in Computing Systems
• 作者: Qian, J.;Sun, Q.;Wigington, C.;Han, H.L.;Sun, T.;Healey, J.;Tompkin, J.;and Huang, J.
• 通讯作者: and Huang, J.