Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. The efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

中文翻译：

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随时间变化特征的置信度指导技术

应用科学家经常采用特征跟踪算法来捕获其仿真数据中各种特征的时间演变。然而，随着科学特征的复杂性随着先进的仿真建模技术的增加而增加，特征跟踪算法的可靠性的量化变得越来越重要。任何鲁棒的特征跟踪算法的要求之一是在每个跟踪步骤中估计其置信度，以便可以毫无歧义地解释获得的结果。为了解决这个问题，我们开发了一种置信度引导的特征跟踪算法，该算法可以可靠地跟踪用户选择的特征，并使用基于图形的可视化以及所跟踪特征的空间可视化来呈现跟踪动态。