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: 0324932
• 负责人: Daniel Lynch
• 金额: $ 7.2万
• 依托单位: Dartmouth College
• 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 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0324932&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.

人类智能的大部分可以概括为识别模式的能力，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，它占用了50%的皮层，70%的感觉受体都是视觉受体，但它只是刚刚能够显示人类视觉系统能够吸收的信息。拟议的研究有以下五个要素：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）通过开发人在回路优化技术来开发和评估感知上接近最优的解决方案，c）一组实验，其将测量显示公共信息结构（包括图形中的路径）的不同方式的效率，流动模式的各个方面，以及重叠表面的形状，d）在灵活的自适应或可调过程中支持数据属性和视觉显示原语之间的映射的算法的开发，e）将这些技术应用于三个领域的可视化问题：流动可视化，重叠表面可视化和大型网络可视化。拟议研究的智力价值将是牢固地建立信息心理物理学，并将该领域定义为一个智力奋进，从而将现有技术结合成一个有凝聚力的学科。高分辨率显示器将使研究人员能够在人类感知能力的极限下工作，从而赋予研究结果长期价值。拟议研究的更广泛影响将包括设计指南，立即可用的设计解决方案，以及算法和信息显示理论。材料的贡献将在以下领域：流动可视化，网络可视化，重叠表面可视化。