Saliency-guided Graphics and Visualization

显着性引导图形和可视化

• 批准号: 0541120
• 负责人: Amitabh Varshney
• 金额: $ 32.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0541120&HistoricalAwards=false
• 关键词: Saliency; guided; Graphics; Visualization

Saliency-Guided Graphics and VisualizationVisual data is growing at an incredible rate due to advances in acquisition, modeling, and simulation technologies. This is posing significant challenges in its processing, display, and comprehension. Current graphics and visualization systems effectively assume a default; that every piece of data is equally important. As the data and displays get larger, visually finding useful information is becoming ever more challenging amidst the visual clutter. This research improves on the default uniform importance by associating every geometric primitive with a salience, an indication of its visual importance, and uses it to design and guide the rendering process. This research draws upon prior work in visual perception, vision, graphics, and scientific visualization and not only addresses how to present data but also what parts of the data to present for effective and compelling visual communication. This research involves advances in algorithms, tools, and techniques to generate effective visual content to facilitate comprehension and efficient rendering.This research centers on defining and using saliency in graphics and visualization and involves developing a suite of methods for computing and assigning saliency to visual data. The investigators study how saliency can guide the rendering process to generate compelling visual content and to allocate computational and rendering resources where they have the most visual impact. The research involves rigorously validating saliency models through user studies using eye tracking and comparing them to human salience judgments. These tasks are in the driving application domains of computational biology and high-energy physics. Compelling scientific visual content has the potential to impact science education besides enhancing the accessibility of science to the common public.

显著性引导的图形和可视化由于采集、建模和仿真技术的进步，可视化数据正以令人难以置信的速度增长。这对它的处理、显示和理解提出了重大挑战。当前的图形和可视化系统有效地假设了一个默认值：每一个数据都同样重要。随着数据和显示变得越来越大，在视觉混乱中寻找有用的信息变得越来越具有挑战性。本研究通过将每个几何图元与显著性（其视觉重要性的指示）相关联来改进默认的统一重要性，并使用它来设计和指导渲染过程。这项研究借鉴了以前在视觉感知，视觉，图形和科学可视化方面的工作，不仅解决了如何呈现数据，而且还解决了数据的哪些部分可以呈现有效和引人注目的视觉传达。该研究涉及算法，工具和技术的进步，以产生有效的视觉内容，以促进理解和高效rendering.This研究中心的定义和使用显着性的图形和可视化，并涉及开发一套方法来计算和分配显着性的视觉数据。研究人员研究了显着性如何引导渲染过程生成引人注目的视觉内容，并将计算和渲染资源分配到最具视觉影响力的地方。 该研究涉及通过使用眼动跟踪的用户研究严格验证显着性模型，并将其与人类显着性判断进行比较。这些任务是在计算生物学和高能物理学的驱动应用领域。引人注目的科学视觉内容有可能影响科学教育，除了提高科学对普通公众的可及性。