Perception-based Information Visualization

基于感知的信息可视化

• 批准号: 410883423
• 负责人: Professor Dr. Oliver Deussen
• 金额: --
• 依托单位: Arbeitsgruppe Visual Computing
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/410883423?language=en
• 关键词: Perception; based; Information; Visualization

Visualizations are perceived by humans like any other type of images, thus it is crucial to select appropriate display parameters for faithfully transporting the underlying data in a visualization. This project aims to analyse to what extent methods known from computer graphics, vision and perception can be applied to judge and appropriately create information visualization results. In their previous works the applicants have shown that, e.g., selecting the right aspect ratio can fundamentally alter the perception of projected high-dimensional data and determine which visualization method is best for showing it. Choosing an appropriate aspect ratio is also important for rendering line graphs and other visualizations. In both works discrete visualizations were represented by density maps that were created by using Kernel Density Estimation (KDE) with Gaussian kernels. In the proposed project this approach will be extended and investigated for which visualization types which kind of continuous representations can be used and how to analyse them by automatic means in order to find optimal display parameters. The applicants will work on several aspects: besides selecting appropriate aspect ratios and other graphical attributes based on continuous representations, they will examine functional plots, common statistical graphics (such as lines charts or bar charts) and study how decorations such as tick marks and background grids alter their perception. A good assignment of such elements might help users to focus on important or interesting aspects of the data. Another aspect is selecting the right colors. Color contrasts and balances help to optimize selection and classification tasks, in a preliminary study the applicants demonstrate that the right color assignment helps in visually distinguishing clusters in a multi-cluster visualization.On both, the discrete data of a visualization and its continuous representation, the applicants want to apply perceptual laws for judging expressiveness. One of the applicants (Deussen) has shown in his previous works that Gestalt-based laws can be used for analyzing object patterns in images and 3d geometry. This will now be applied to the elements of visualizations. Furthermore, a number of aesthetics laws exist for the analysis of images that have been used in computer graphics and vision. The applicants will use this to optimize visual parameters of visualizations and at the same time they will use the underlying (discrete) data to optimize aspects that cannot be seen in the continuous representation, such as outliers, very sparsely populated areas. By combining aspects of both representations perceptually optimal visualization parameters will be estimated. Thus the applicants want to lay the foundations of perceptually-driven information visualization by jointly investigating continuous and discrete factors that influence how visualizations are perceived.

可视化像任何其他类型的图像一样被人类感知，因此选择适当的显示参数以忠实地传输可视化中的基础数据至关重要。该项目旨在分析计算机图形学、视觉和感知中的已知方法在多大程度上可以应用于判断和适当地创建信息可视化结果。在他们之前的工作中，申请人已经表明，例如，选择正确的纵横比可以从根本上改变对投影高维数据的感知，并确定哪种可视化方法最适合显示它。选择适当的纵横比对于渲染线图和其他可视化也很重要。在这两部作品中，离散可视化均由密度图表示，这些密度图是通过使用带有高斯核的核密度估计 (KDE) 创建的。在拟议的项目中，这种方法将得到扩展并研究哪些可视化类型可以使用哪些类型的连续表示，以及如何通过自动方式分析它们以找到最佳显示参数。申请人将在几个方面进行工作：除了根据连续表示选择适当的纵横比和其他图形属性外，他们还将检查功能图、常见统计图形（例如折线图或条形图）并研究刻度线和背景等装饰如何网格改变了他们的看法。这些元素的良好分配可能会帮助用户关注数据的重要或有趣的方面。另一个方面是选择正确的颜色。颜色对比和平衡有助于优化选择和分类任务，在初步研究中，申请人证明正确的颜色分配有助于在多聚类可视化中视觉区分聚类。在可视化的离散数据及其连续表示方面，申请人希望应用感知法则来判断表现力。其中一位申请人 (Deussen) 在他之前的作品中表明，基于格式塔的定律可用于分析图像和 3D 几何中的对象模式。现在这将应用于可视化元素。此外，对于计算机图形和视觉中使用的图像分析，存在许多美学定律。申请人将使用它来优化可视化的视觉参数，同时他们将使用底层（离散）数据来优化连续表示中看不到的方面，例如异常值、人口稀少的区域。通过结合两种表示的各个方面，将估计感知最佳可视化参数。因此，申请人希望通过共同研究影响可视化感知方式的连续和离散因素，为感知驱动的信息可视化奠定基础。