中文摘要：

目的 在传统基于时序曲线的时变体数据特征的识别与跟踪过程中,不仅需要用户具备丰富的先验知识来确定感兴趣特征的时序曲线形状,而且时序曲线段的匹配和抽取过程亦复杂,难以交互,这一定程度上降低了时变体数据可视化及分析效率.提出了一种新的基于高斯时序曲线的时变体数据可视化及分析方法.方法 首先,对时序曲线进行低通滤波,准确地查找极小值点,对时序曲线进行分段;进而,引入非对称高斯函数对时序曲线段进行拟合获得高斯时序曲线;为了进一步方便用户识别感兴趣特征,观察特征在时间域上的演变过程,提供一种便捷的交互技术,由用户单击任一时间步绘制结果图像中感兴趣的特征信息,分析视线方向上的特征可见性,以确定感兴趣特征及其对应的高斯曲线,进而由参数匹配获得所有时间步中感兴趣特征信息.结果 为了验证本文算法的高效性与可行性,对时序仿真的飓风数据进行特征跟踪与交互式可视化,可以看出,本文算法不仅可以准确跟踪飓风中心特征,而且特征分析与可视化效率亦大大提升.结论 相比于传统的时变体数据可视化方法,本文算法不需要用户先验知识的前提下,利用非对称高斯模拟时序曲线变化,进而由高斯参数匹配代替复杂的时序曲线匹配过程,有效地提升了时变体数据可视化及分析效率.

英文摘要：

Objective Matching and extracting the target curve from a large dataset are time-consuming and laborious tasks when traditional feature detection and tracking methods based on the temporal curve analysis are used because prior knowledge is required to determine the shape of the temporal curve of the region of interest. In this study, we introduce a novel method based on asymmetric Gaussian function to conduct real-time feature detection and tracking for time-varying volume visual analysis. Method A low-pass filter is first employed to smoothen the original temporal curve, which helps achieve the minimum data points accurately. The original temporal curve can be further subdivided into several segments according to the minimum points. We then take advantage of asymmetric Gaussian functions to fit the segments and to achieve a new Gaussian temporal curve, which can be further expressed on the basis of several simple Gaussian parameters. A convenient interface in which users can select the features of interest by means of visibility analysis along the viewing rayis also provided. Therefore, the evolution of the selected features can be further tracked in different time steps by matching the corre- sponding Gaussian parameters. Result To demonstrate the effectiveness of the proposed method, time-varying simulative data are used to conduct feature detection and interactive visualization. A large number of experimental resuhs indicate that the proposed method can detect time-varying features accurately and quickly. Conclusion The proposed technique expresses the basic temporal curve with new asymmetric Gausslan temporal curve and then takes the simple Gaussian parameter matching as a substitute for the complicated temporal curve matching process. Compared with traditional methods, the proposed approach can conduct feature detection and tracking in real time.