基于信息熵种子点选取的流线可视化  

Two information entropy-based seeding methods for 3D flow visualization

作　　者：黄冬梅[1] 杜艳玲[1,2] 张律文 HUANG Dong-mei;DU Yan-ling;ZHANG Lii-wen(College of Information Technology,Shanghai Ocean University,Shanghai 201306;East China Sea Forecast Center of the State Oceanic Administration,Shanghai 200136,China)

机构地区：[1]上海海洋大学信息学院,上海201306 [2]国家海洋局东海预报中心,上海200136

出　　处：《计算机工程与科学》2018年第3期411-417,共7页Computer Engineering & Science

基　　金：国家自然科学基金(61272098;41671431);国家重点基础研究发展规划(2012CB316200-G)

摘　　要：有效的种子点选取方法是影响流线分布洞悉流场特性的关键。在保持流场变化规律与重要特征准确描述前提下,为了解决由过多流线所导致的遮挡与杂乱问题,提出了基于贪婪策略和蒙特卡洛的两种种子点选取方法。基于贪婪策略的种子点选取方法通过流场信息熵的计算,对流场中的关键特征具有高度敏感性。基于蒙特卡洛种子点选取方法根据均匀随机分布函数生成输入,基于信息熵计算输入点影响半径确定流线分布。通过多个数据集对两种选取方法实验,结果表明基于贪婪策略选取方法可高效捕获流场的关键特征,基于蒙特卡洛方法选取流线更加均匀,保持了流场全局变化规律,两种方法的结合得到更优化的流场可视化效果。Effective seeding method is the key to influence the streamline distribution and to under-stand the underlying properties of flow field.Based on the accurate description of flow field variation and important features,this paper proposes two information entropy-based seeding methods to solve the well-known occlusion and cluttering issue.The first greedy seeding method locates interesting areas through the calculation of entropy values.The greedy seeding method is highly sensitive to the impor-tant features.The second Monte Carlo seeding method generates random inputs based on a probability distribution,and then defines the influence areas of input grid points as a circle in 2D and a sphere in 3D.Comprehensive experiments on multiple datasets show that the greedy seeding method can capture the important features efficiently and the Monte Carlo seeding method shows significant ability to obtain global variation.Besides,the combination of both methods can get more optimal flow field visualization.

关 键 词：流场可视化 信息熵 种子点 流线 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]