GAMES Webinar 2024 – 345期(交互式设计在数据降维和矢量图创建中的应用) | 田宗霖(北京印刷学院)，周珺(耶鲁大学)
【GAMES Webinar 2024-345期】(可视化专题-交互式设计在数据降维和矢量图创建中的应用)
报告嘉宾：田宗霖(北京印刷学院)
报告时间：2024年10月24号星期四晚上8:00-8:30（北京时间）
报告题目：解释高维数据的投影
报告摘要：
可视化技术通常是科学家在基于多维数据集形成、完善或否定潜在假设时的重要辅助工具。其中，降维技术（也称为投影）在处理样本数量和维度扩展方面表现出显著优势，因而成为最常用的多维数据可视化手段之一。然而，投影生成的图像往往难以解读，用户在没有额外的视觉辅助下几乎无法进行详细分析。因此，我们研究了如何通过交互式视觉解释机制来增强现有投影技术的可解释性，并通过实验设计定量评估了交互式视觉解释在特定任务中给用户带来的增益。此次报告将介绍我们围绕交互式视觉解释这一主题展开的系列研究，以及我们面向真实数据集设计开发的可视分析辅助工具的最新成果。
讲者简介：
Zonglin Tian received his Ph.D. from Utrecht University in 2023, and is now working at the Beijing Institute of Graphic Communication. He received his Bachelor’s and Master’s degrees from Huazhong Agricultural University and Northeastern University in 2016 and 2019, respectively.His research focuses on explaining projections of high-dimensional data by enriching existing projection techniques through interactive visual explanatory mechanisms.
讲者主页：https://tianzonglin.com/cv/
报告嘉宾：周珺(耶鲁大学)
报告时间：2024年10月24号星期四晚上8:30-9:00（北京时间）
报告题目：Approximate Feature Adaptive Rendering for Subdivision Gradient Meshes 细分矢量网格的自适应渲染方法
报告摘要：
梯度网格是一种高级矢量图形元件，被设计师广泛用于创建可缩放矢量图形。传统的方法要求具有规则的矩形拓扑结构，对设计有严格的限制。而细分梯度网格允许使用细分技术来定义生成的彩色表面的任意流形拓扑结构。这也允许艺术家在不同细分级别的几何形状和颜色上进行操作。最近的方法允许在几何形状和颜色之间进行插值，在较粗的细分级别上进行编辑后的局部细节和锐利的颜色过渡。所有现有方法的缺点是它们依赖于全局细化，这使得它们不适合实时（商业）设计应用。我们提出了一种新颖的方法，结合了自适应细分的理念，并使用适合硬件细分的近似曲面（approximating patches），以实现实时性能。此外，该方法还具有其他新颖的功能，包括多种交互机制和在交互式设计/编辑期间防止自相交。
讲者简介：
Jun Zhou is a Postdoctoral Associate at the Yale Graphics Group, working under the mentorship of Prof. Holly Rushmeier. Her research centers on computer vision, vector graphics, and numerical integration. Prior to joining Yale, she completed her PhD in Computer Graphics at the University of Groningen in 2023, under the supervision of Prof. Jiří Kosinka and Prof. Jos Roerdink.
讲者主页：https://junzhoupro.github.io/
主持人简介：
Chengtao Ji received his M.S. and Doctoral degree from Sichuan University and Groningen University in 2014 and 2018, respectively. He is currently an assistant professor in Xi’an Jiaotong-Liverpool University, China. His research interests include data mining, complex network analysis, and visual analytics.
Yunzhe Wang received her M.S. and PhD in Computer Science from The University of Hong Kong and The Hong Kong Polytechnic University in 2014 and 2020, respectively. She is currently a lecturer in Suzhou University of Science and Technology, China. Her research interests include data science, social networks, and visual analysis. She has published paper on top conferences and journals including Computer Graphics Forum and ACM Transactions on Knowledge Discovery from Data and so on. She received ICCI*CC Best Paper Award in 2015, SIGGRAPH Asia Sym. Vis. Best Paper Award in 2017 and Jiangsu Innovation and Entrepreneurship Ph.D. from World Prestigious Universities Award in 2021. She now hosts a project supported by the Young Scientists Fund of the National Natural Science Foundation of China.
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