A 3D streamline selection algorithm based on comprehensive similarity

In order to avoid the occlusion problems and missing important features of streamlines in the flow field, this paper proposes a 3D streamline selection algorithm based on comprehensive similarity. The method starts with a hierarchical clustering of streamline sets and then extracts streamlines with high similarity based on their comprehensive similarity. The comprehensive similarity of streamlines requires the calculation of the distance and contour similarity of the streamlines. In this paper, the Hausdorff distance is improved by proposing a partially matched Hausdorff distance to reduce the influence of streamline length on the similarity calculation. Then the contour similarity is calculated according to the ICP algorithm, and the entropy weighting method is used to calculate the weights to obtain the combined similarity. The final comparison with the result of another algorithm shows that the flow field structure is clearer, more complete and more evenly distributed when the streamlines are selected using this algorithm.