摘要
目的自然界中的大部分现象本质上都是在空间上随时间的流逝不断发展变化的物理或化学过程,可以表述为含有时间变量的数据场,这些数据场称为时变体数据。随着科学计算技术、计算机仿真技术以及现代观测技术的发展,能够以前所未有的精度对自然现象进行仿真或者观测,但同时也面临时变体数据体积大、时间长以及变量数目多的难题。为了更有效地显示时变体数据并挖掘数据中的关键信息,针对时变体数据的可视化,本文提出一种基于数据特征的方法,用于探索时变体数据中感兴趣区域(即特征)的特点与变化。方法通过将特征提取、特征跟踪、运动检测和提出的3种特征可视化方法(数据帧特征可视化、单个运动过程特征可视化和空间多运动过程特征可视化)置于同一个框架之中,提供一种从时间域和空间域探索多变量时变体数据的一站式解决方案,并突出时变体数据的动力学特性。结果本文方法在4组不同的时变体数据上应用,对数据中特征各变量的变化以及感兴趣的运动进行了特征可视化。结论实验结果显示本文方法能以较小的时间成本有效显示数据中的特征以及用户定义的运动,方法的有效性与实用性得到了验证。
Objective Scientific phenomena,such as combustion,ocean currents,and hurricanes are inherently time-varying processes that can be represented as data fields with time variables.Data fields with time variables are often referred to as time-varying volume data.Studying the dynamic aspects of scientific phenomena that change over time is critical to the solution of many scientific problems.With the rapid advancement in computing technologies,time-varying volume data have been created to simulate many physical and chemical processes in their spatial and temporal domains with unprecedented accuracy and complexity.Time-varying volumes usually have large sizes(millions or even billions of voxels),long duration(hundreds or even thousands of timesteps),and contain multiple variables.Presenting time-varying volume data providing a powerful impetus for the research on the visualization of time-varying volume data.It is important to first present the data information efficiently then allow scientists to have direct interaction with the data and glean insights into the simulated scientific phenomena.The ability of scientists to visualize time-varying phenomena is essential to ensuring the correct interpretation and analysis,fostering insights,and communicating those insights to others.Rendering time-varying volume data to achieve interactive visualization has long been of interest to the visualization community.Method s for visualizing time-varying volume data can be classified broadly into two types:time-independent and time-dependent.Time-independent algorithms process each timestep or multiple timesteps of time-varying data independently and display a sequence of timesteps as an animation.Method s generally include encoding data to make it more manageable(e.g.,down-sampling in the time domain,data compression,contour extraction),preselecting transfer functions for direct volume rendering,and interactive hardware-accelerated volume rendering.Time-independent algorithms,which do not rely on domain and expert knowledge,have t
作者
刘力
Liu Li(School of Computer Science and Technology,Soochow University,Suzhou 215301,China)
出处
《中国图象图形学报》
CSCD
北大核心
2022年第4期1302-1313,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(62002253)。
关键词
时变体数据
特征可视化
特征跟踪
运动检测
交互式可视化
time-varying volume data
feature visualization
feature tracking
activity detection
interactive visualization
