关键词: 矢量场可视化, 信息熵, 流线质量评估, 流线布局

Abstract: Flow visualization based on streamlines, which is applied in many fields, is a very important branch of visualization. However it’s difficult to effectively measure how much information of the origin flow field have been displayed by streamlines generated with existing methods. A new method which can evaluate streamlines and place seeds based-on information entropy is presented in this paper. Firstly, the seeds are placed based on Shannon entropy which can seize the important features in the field, and then the features are strengthened by templates. The conditional entropy between intermediate flow field and origin vector field are used to quantify the uncertainty remained in original field after streamlines are shown. An importance-based seed sampling method adds new seeds iteratively until the conditional entropy between intermediate flow field and origin vector field converges. Experiments show that this algorithm can show enough information to recover the origin field with limited streamlines.

Key words: flow visualization, information entropy, streamlines quality evaluation, seeds placement