Feature-Based Streamline Selection Method for 2D Flow Fields

Missing features often occurs in both streamline placement and streamline selection. To solve this problem, a feature-based approach to streamline selection that can guarantee the preservation of the integrity of flow field features is presented in this paper. The streamline feature type is defined based on the relationship between streamlines and flow field features. Given a large number of randomly or uniformly seeded streamlines, the proposed approach judges the feature type for each streamline by considering its geometric characteristics and amount of the information. All streamlines are then clustered according to their feature types and positions. Streamlines are selected from those feature streamline clusters. In addition, a streamline similarity measure based on the dynamic time warping algorithm and the mean of closest point distances is presented to avoid selecting redundant streamlines. The developed algorithm is applied to multiple data sets and compared with a recent streamline selection algorithm. Results show that the developed algorithm can reflect the key features of a flow field more effectively and greatly improve the readability of streamline visualizations.