Saliency-guided Graphics and Visualization

显著性引导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。这对其处理、显示和理解提出了重大挑战。当前的图形和可视化系统实际上是默认的；每条数据都同样重要。随着数据和显示器变得越来越大，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原语与其视觉重要性的显着性联系起来，改进了默认的统一重要性，并使用它来设计和指导渲染过程。本研究借鉴了先前在视觉感知、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，以促进理解和有效的呈现。本研究以定义和使用图形和可视化中的显著性为中心，并涉及开发一套用于计算和分配视觉数据显著性的方法。研究者研究如何显著性可以引导渲染过程，以产生引人注目的视觉内容，并分配计算和渲染资源，他们有最大的视觉影响。这项研究包括通过使用眼动追踪的用户研究严格验证显著性模型，并将其与人类显著性判断进行比较。这些任务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除了提高公众对科学的可及性外，还可能影响科学教育。

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.