Saliency-guided Graphics and Visualization

突出制导图形和可视化由于采集、建模和模拟技术的进步，可视数据正以惊人的速度增长。这给它的处理、显示和理解带来了巨大的挑战。当前的图形和可视化系统实际上假定了一个默认条件：每一条数据都是同等重要的。随着数据和显示变得越来越大，在视觉混乱中从视觉上找到有用的信息变得越来越困难。本研究通过将每个几何基元与一个突显性相关联来改进默认的一致重要性，并使用该突显性来设计和指导渲染过程。这项研究借鉴了前人在视觉、视觉、图形和科学可视化方面的工作，不仅讨论了如何呈现数据，而且还讨论了为了有效和引人注目的视觉交流而呈现数据的哪些部分。这项研究涉及到在算法、工具和技术方面的进展，以生成有效的视觉内容以促进理解和高效的渲染。这项研究的中心是定义和使用图形和可视化中的显著度，并涉及开发一套计算和分配视觉数据显著度的方法。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，并在它们具有最大视觉影响的地方分配计算和渲染资源。这项研究涉及通过使用眼球跟踪的用户研究严格验证显著性模型，并将它们与人类的显著性判断进行比较。这些任务是在计算生物学和高能物理的驱动应用领域。引人入胜的科学视觉内容除了提高公众对科学的可及性外，还有可能影响科学教育。

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.