Method Of Streamlines Quality Evaluation And Seeds Placement Based-on Information Entropy

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 are completed with this method, on datasets for a slice of hurricane Isabel, a Rayleigh-Benard unsteady flow field and the numerical simulation of flow past a tapered cylinder. The streamlines generated shows that the algorithm can emphasize features according to local entropy values and minimizes visual cluttering and creates streamlines with better visual focus. The results of these experiments show that this algorithm can show enough information to recover the origin field with limited streamlines.