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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Similarity-Guided Streamline Placement with Error Evaluation
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Similarity-Guided Streamline Placement with Error Evaluation

机译：具有误差评估的相似度指导的流线布置

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摘要

Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 2D and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field.
机译：大多数流线生成算法要么在整个域中提供特定密度的流线，要么显式检测特征（例如关键点），并遵循自定义规则以强调那些特征。但是，前者通常包括许多冗余流线，而后者则需要关于哪些点是要素的布尔决策（因此可能会遇到现实数据的健壮性问题）。我们采用一种新的方法来对2D和3D中的稳定矢量场进行自适应流线放置。我们为流线之间的局部相似性定义了一个度量，并使用该度量从一组密集的候选种子点中生长流线。该度量不仅考虑欧几里得距离，而且还考虑形状和方向相似性的简单统计量度。在没有显式特征检测的情况下，我们的方法产生的流线自然突出了几何感兴趣的区域。结合此方法，我们还提出了一种定量误差度量，用于根据流线表示保留原始向量字段中的信息的程度来评估流线表示。该误差度量从流线表示中的点重建向量场，并计算与原始向量场的重建差。

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