ITR Collaborative Research: Perceptual Optimization for Data Visualization

ITR 协作研究：数据可视化的感知优化

• 批准号: 0324306
• 负责人: David Laidlaw
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0324306&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Perceptual; Optimization

Much of human intelligence can be broadly characterized as the ability to identify patterns and the visual system is the most sophisticated pattern finding mechanism that we have. Of all of our Perceptual systems, vision dominates. It is estimated to engage 50% of the cortex and 70% of all our sensory receptors are visual, but is only just becoming possible to display as much information as the human visual system is capable of absorbing. The proposed research has the following five elements: a) A display that supports real-time animation at the limit of the resolution of the human visual system, b) The development and evaluation of perceptually near-optimal solutions through the development of human-in-the-loop optimization techniques, c) A set of experiments that will measure the efficiency of different ways of displaying common information structures, including paths in graphs, aspects of flow patterns, and the shapes of overlapping surfaces, d) The development of algorithms that support the mapping between data attributes and visual display primitives in a flexible adaptive or tunable process, e) The application of the techniques to visualization problems in three areas: flow visualization, overlaying surface visualization and large network visualization. The intellectual merit of the proposed research will be to firmly establish information psychophysics and define the field as an intellectual endeavor thereby combining existing techniques into a cohesive discipline. The high- resolution display will enable researchers to work at the limits of human perceptual capability endowing the results with long-term value.The broader impacts of the proposed research will include design guidelines, immediately useable design solutions, as well as algorithms and information display theory. Material contributions will be made in the following areas: flow visualization, network visualization, and overlapping surface visualization.

人类的大部分智能可以广泛地描述为识别模式的能力，而视觉系统是我们拥有的最复杂的模式发现机制。在我们所有的感知系统中，视觉占主导地位。据估计，50%的大脑皮层和70%的感官感受器是视觉的，但它只能显示人类视觉系统能够吸收的尽可能多的信息。所提出的研究具有以下五个要素：a)在人类视觉系统的分辨率极限下支持实时动画的显示器，b)通过开发人在回路中的优化技术来开发和评估感知接近最优的解决方案，c)将测量显示常见信息结构的不同方式的效率的一组实验，包括图形中的路径、流动模式的方面和重叠表面的形状，d)开发在灵活的自适应或可调过程中支持数据属性和视觉显示基元之间的映射的算法，E)该技术在流动可视化、覆盖面可视化和大型网络可视化三个领域的可视化问题中的应用。拟议研究的智力价值将是坚定地建立信息心理物理学，并将该领域定义为智力努力，从而将现有技术结合成一门有凝聚力的学科。高分辨率显示将使研究人员能够在人类感知能力的极限下工作，赋予结果长期价值。拟议的研究将产生更广泛的影响，包括设计指南、立即可用的设计解决方案，以及算法和信息显示理论。将在以下方面做出实质性贡献：流动可视化、网络可视化和重叠曲面可视化。