自适应可调混沌粒子群算法的体绘制视点选择

doi:10.16182/j.issn1004731x.joss.201812013

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (12): 4595-4601.doi: 10.16182/j.issn1004731x.joss.201812013

自适应可调混沌粒子群算法的体绘制视点选择

曾艳阳, 张泽浩, 冯云霞

河南理工大学计算机科学与技术学院,河南焦作 454000

收稿日期:2018-05-28 修回日期:2018-06-15 出版日期:2018-12-10 发布日期:2019-01-03

Volume Rendering Viewpoint Selection Based on Adaptive Scaleable Chaotic Particle Swarm Optimization

Zeng Yanyang, Zhang Zehao, Feng Yunxia

College of Computer Science and Technology of Henan Polytechnic University, Jiaozuo 454000, China

Received:2018-05-28 Revised:2018-06-15 Online:2018-12-10 Published:2019-01-03
Supported by:
National Natural Science Foundation of China (61503124).

摘要/Abstract

摘要： 人们常通过人工试探的方法进行体绘制视点选择,然而当体数据较多时往往花费大量的时间而降低效率。提出将自适应可调的混沌粒子群优化算法用于体绘制中视点的自动选择。该算法引入图像信息熵来评估在不同视点处对应的图像的质量。熵值作为视点优化的依据和粒子群优化的适应度值,从而实现了智能化和自动化的选择,最终得到最佳视点。

关键词: 粒子群优化算法, 体绘制, 视点选择, 三维可视化

Abstract: People often perform volume rendering viewpoint selection by means of manual exploration. However, when the volume data is large, it often takes a lot of time to reduce the efficiency. An adaptive scaling chaotic particle swarm optimization algorithm is proposed for the automatic selection of viewpoints in volume rendering. The algorithm introduces image information entropy to evaluate the quality of images corresponding at different viewpoints. The entropy value is used as the basis for optimizing the viewpoint and the fitness value of the particle swarm optimization, so as to realize the intelligent and automatic choice, and finally obtain the best viewpoint.

Key words: particle swarm optimization algorithm, volume rendering, viewpoint selection, 3D visualization

中图分类号:

TP391.9

曾艳阳, 张泽浩, 冯云霞. 自适应可调混沌粒子群算法的体绘制视点选择[J]. 系统仿真学报, 2018, 30(12): 4595-4601.

Zeng Yanyang, Zhang Zehao, Feng Yunxia. Volume Rendering Viewpoint Selection Based on Adaptive Scaleable Chaotic Particle Swarm Optimization[J]. Journal of System Simulation, 2018, 30(12): 4595-4601.

参考文献

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自适应可调混沌粒子群算法的体绘制视点选择

Volume Rendering Viewpoint Selection Based on Adaptive Scaleable Chaotic Particle Swarm Optimization

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