一种基于粒子群优化的传递函数设定方法
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摘要
传递函数的选取是直接体绘制技术的重要研究内容之一.文中将传递函数的选取过程视为一参数优化问题,提出一种基于粒子群优化方法的传递函数设定方法.粒子群算法以自动式和交互式两种进化方式应用于体绘制传递函数设定领域,且在交互式实现方式中提出了基于用户的速度更新策略.头部数据和地震数据显示的实验结果表明:与基于遗传算法的传递函数设定方法相比,文中的基本型和改进型粒子群算法具有更好的显示性能,在耗时较小的情形下,所绘制的地层结构体绘制图像具有更高的质量.
Transfer function(TF) specification is an important research issue in direct volume rendering(DVR).In this paper,the problem has been developed as a parameter optimization model and a modern evolutionary algorithm,the particle swarm optimization(PSO),has been applied to solve it effectively.Initial solutions are formed and encoded into particles of PSO,the particles fly intelligently in the search space to achieve the best sequence.We realize the implementation of the algorithm in automatic and interactive approaches,and modify updating strategy emphasizing the user requirement in the interactive method.Experiments results confirm the performance and efficiency of the automatic implement of origin algorithm with a comparison to genetic algorithm(GA) which has been applying to this field previously.A contrast has been made between the conventional and modified PSO algorithm and GA in interactive implement to verify the feasibility and applicability of modified PSO algorithm.
Transfer function(TF) specification is an important research issue in direct volume rendering(DVR).In this paper,the problem has been developed as a parameter optimization model and a modern evolutionary algorithm,the particle swarm optimization(PSO),has been applied to solve it effectively.Initial solutions are formed and encoded into particles of PSO,the particles fly intelligently in the search space to achieve the best sequence.We realize the implementation of the algorithm in automatic and interactive approaches,and modify updating strategy emphasizing the user requirement in the interactive method.Experiments results confirm the performance and efficiency of the automatic implement of origin algorithm with a comparison to genetic algorithm(GA) which has been applying to this field previously.A contrast has been made between the conventional and modified PSO algorithm and GA in interactive implement to verify the feasibility and applicability of modified PSO algorithm.
引文
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[2]Fourie P,Groenwold A.The particle swarm opti mizationalgorithmin size and shape opti mization[J].Structural andMultidisciplinary Opti mization,2002,23(4):259-267.
[3]Kennedy J,Eberhart R C.Particle swarm opti mization[C]//Proceedings of IEEE international conference onneural networks.Piscataway,NJ,1995:1942-1948.
[4]Pfster H,Lorensen B,Bajaj C,et al.The transfer func-tion bake-off[J].IEEE Computer Graphics and Applica-tions,2001,21(3):16-22.
[5]He T,Hong L,Kauf man A,et al.Generation of transferfunctions with stochastic search techniques[C]//Proceed-ings of IEEE Visualization.Los Alamitos,CA,USA,IEEE Computer Society,1996:227-236.
[6]Francisco de MPinto,Carla MD S Freitas.Volume visu-alization and exploration through flexible transfer functiondesign[J].Computers&Graphics,2008(32):540-549.
[7]Sereda P,Bartroli A V,Gerritsen F A.Automatingtrans-fer function design for volume rendering using hierarchicalclustering of material boundaries[C]//Proceedings of Eu-rographics/IEEE VGTC Symposiumon Visualization 2006Eurographics.Neder land,2006:243-250.
[8]Pinto F de M,Freitas C MD S.Design of multi-di men-sional transfer functions using di mensional reduction[C]//Proceedings of Eurographics/IEEE VGTC Symposium onVisualization 2007.Eurographics,2007:131-138.
[9]Marks J,Andal man B,Beardsley P A,et al.Design gal-leries:a general approachtosetting parameters for comput-er graphics and ani mation[C]//SIGGRAPH97:Proceed-ings of the 24th annual conference on Computer graphicsandinteractive techniques.New York:ACMPress,1997:389-400.
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