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收稿日期：2020-12-28，
修回日期：2021-02-24，
录用日期：2021-3-3，
纸质出版日期：2022-04-16
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目的
2
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
2
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
2
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
2
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
2
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. Methods 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. Methods 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 the advantages of easy operation and good flexibility but fail to consider the dynamic and time-varying characteristics of data. Moreover
the methods cannot highlight the information in data important to scientific discovery. Different from time-independent methods
time-dependent methods
which are usually referred to as "feature-based visualization" or "feature visualization"
focus on the features of data and track the variation tendency of data by using the consistency of feature movements and interactions between adjacent timesteps. In this context
a feature can be defined from two aspects: 1) regions of interest that can be extracted from original data
such as shape
structure
variation
and phenomena and 2) some subsets of interest in the original data. Using techniques from image processing and mathematical morphology
feature visualization algorithms extract amorphous regions from scalar or vector fields of data and create correspondences between consecutive timesteps with certain matching criteria. A major advantage of feature visualization over other methods is that it exploits the data coherence between consecutive timesteps and focus on just those regions of interest so that users can ignore redundant
unimportant
or noninterest regions. The resulting significant reduction in the storage requirement of data and rendering cost of visualization tasks makes feature visualization most suitable for investigating a temporal-spatial variation and motion process. Feature visualization of time-varying volume data generally includes four major steps: 1) defining features of data according to domain knowledge or research need; 2) extracting and quantifying features from data; 3) tracking the extracted features step by step; and 4) presenting features by isosurface rendering or direct volume rendering.
Method
2
In this paper
a method based on feature visualization is proposed to help scientists explore the characteristics and variations of regions of interest in time-varying volume data. The proposed method includes a feature-based data processing part which combines feature extraction
feature tracking over time
and event query and isolation in one workflow and three interactive visualizations: feature visualization of a data frame
feature visualization of an individual event
and feature visualization of multiple events in the spatial context. In the feature-based data processing part
a region-growing algorithm with a threshold provided by users is applied to the scalar field of the primary variable set by users (or the only variable) in every data frame. Connected components of points with data values above the threshold are extracted from each data frame as features. Geometric properties
such as volume
mass
and centroid are calculated for each extracted feature during the process of extraction. Using the point metadata generated from feature extraction
the extracted features in each timestep are correlated over time with a feature tracking algorithm based on volume overlap to determine occurrences of the same feature in consecutive time steps. A feature may go through five states in its evolution: birth
continuation
merge
split
and death. With numerous features spanning dozens or even hundreds of timesteps
it is necessary to isolate all occurrences of the same features from the tracking history to help understand the dynamics in the data. In this context
the temporal and spatial evolution of a feature is referred to as an event. A state graph-based event query algorithm is utilized to capture events defined by scientists. After the event query
a list is created to record all isolated events as a sequence of the triplet: timestep
index of the feature in this timestep
and the state in the evolution process. In the visualization part of the method
a web-based viewer is developed to provide a user interface to explore the feature metadata generated from the feature-based data processing program with three interactive visualizations.
Result
2
The proposed method is applied to four time-varying volume datasets: turbulent vortex
hurricane Isabel
ocean simulation
and hydrothermal plume. The visualization results demonstrate the events of interest from each dataset and further allow users to explore the data from different perspectives from an instance to the entire dataset
which confirmed the usability and effectiveness of the proposed method.
Conclusion
2
Generally
the proposed method of this paper has five major benefits: 1) providing a one-stop solution to explore the spatial
temporal
and parameter spaces of time-varying volume data; 2) highlighting the dynamic aspects of time-varying volume data
which helps scientists understand when and where interesting events occur in a dataset; 3) the feature metadata generated by the feature-based data processing algorithm ensures efficient loading and processing a large amount of data; 4) the viewer does not require a client-side installation and offers centralized maintenance
making it more accessible to more users; and 5) the visualization results and the viewer itself can be distributed easily among multiple users
thereby promoting collaborative research.
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