基于智能优化算法的体绘制研究
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摘要
体绘制是应用于工程和医学等领域大规模三维数据场可视化的重要技术手段。它对信息的表达准确且完备,相比面绘制等传统可视化技术,它更符合科学计算的严谨性要求。之前研究中体绘制控制要素的设计,特别是转换函数设计和视点选择严重依赖用户经验,用户交互频繁但效率不高,可用性差。
本文基于智能优化算法实现了转换函数设计和视点选择的自动化,提高了体绘制算法的可用性。自动转换函数设计可更准确且更有目的性地显示体数据;自动视点选择方法则能快速地选取数据观察的最优位置,避免重要信息被遮挡,并获取尽量多的有效信息。
本文研究的重点包括:
1、在基于图像的转换函数设计的基础上,提出了基于粒子群优化和基于遗传粒子群算法的转换函数设计方法。该方法基于不同的转换函数评价方法分别实现了转换函数设计的自动和半自动化,在降低用户操作强度的同时,减少了设计次数,提高了设计效率。同时基于智能优化算法的粒子评价过程提出了一种基于主观评价和客观评价的混合评价方法。该方法在评价过程中综合考虑了用户的主观评价值和粒子的客观评价值,将其按照一定比例合成,得到最终评价值。这种评价方法可使可视化结果既满足用户的需求,又符合严谨的客观评价原则。
2、在简单转换函数设计的基础上,提出了一种复杂转换函数的设计方法。它把复杂转换函数设计问题转化为多个简单转换函数的融合设计问题。这种方法直观且易于实现,降低了复杂转换函数的设计难度。它把融合设计问题转化为对融合比例的参数寻优问题,采取基于预期适应度的相似性评价方法对融合比例作出评价,由PSO根据评价值生成新的融合比例,在很大程度上简化了复杂转换函数的设计。
3、在体绘制中提出了一种基于粒子群优化的自动视点选择方法,基于屏幕提供给用户包含最多数据信息的绘制图像。该方法根据视点信息量来评估视点质量,通过采用PSO迭代生成新视点,较大程度地减少了需评价的视点数目,从而消除了冗余的视点计算,在保证视点质量的同时提高了视点选择效率。
经过大量实验证明,本文提出的算法能较好地缩减用户可视化操作的工作量,有效提高了可视化效率,在体绘制的智能化方面做出了有益的探索。
Visualization of large datasets receives increasing attention from both engineering and medical communities in recent years.Direct Volume Rendering(DVR) has been proven to be an effective and important technique for visualizing 3D large-scale datasets.Compared with the traditional geometry rendering methods,it is more suitable for the visualization of scientific computation,since it exhibits information more accurately without losing any data.Traditional DVR methods heavily depend on users' experience to select the suitable transfer function(TF) and viewpoint,which makes it inefficient and hard to use.
This thesis studies automatic/semi-automatic methods for design of transfer functions and selection of optimal viewpoints based on intelligent optimization algorithms.The study on automatic design methods of transfer function aims to express the information inside volume datasets more accurately and more purposively. Automatic viewpoint selection is used to locate optimal viewpoints quickly which can avoid the occlusion of important data.
The main contributions of this thesis include:
1.Based on image-based TF design,a technique using Particle Swarm Optimization(PSO) and genetic PSO is presented to improve the efficiency of DVR.This method makes the transfer function design automatically,which is based on various ways of TF evaluation.It does not only ease users,but also reduces the time in adjustment.We also provide a mixed evaluation method for particle evaluation,which is based on both subjective and objective evaluations.According to this method,the final fitness value is formed of the subjective evaluation values from users and the objective evaluation values from several energy functions,on a given proportion.With this method,the visualization results can satisfy users,which follow objective principles precisely.
2.Based on the simple TF design,this thesis presents a technique for complicated TF design.It converts complicated TF design problem into the fusing problem of several simple TFs.The keystone is to formualte the TF fusing problem into searching for an optimal fusing proportion,by using a similarity evaluation method,which is based on expectation fitness.To a large extent,it simplifies the design process of complicated TF.
3.As for volume rendering,this thesis brings forward a PSO-based viewpoint selection method,which provides the viewpoint that can improve both the speed and efficiency of data understanding.During the process,it generates new viewpoints using PSO,and the quality of a viewpoint is intuitively related to how much information its corresponding view gives us about a scene.We use viewpoint entropy to define the informative view.This method remarkably reduces the number of viewpoint candidates,thus eliminates the reluctant viewpoint evaluations.Generally speaking,it improves the performance of the applications,and the viewpoint quality as well.
As proved with lots of experiments,these methods can greatly improve DVR efficiency,and ease the burden of users.Practice shows that we have done beneficial exploration for intellectualized visualization.
本文基于智能优化算法实现了转换函数设计和视点选择的自动化,提高了体绘制算法的可用性。自动转换函数设计可更准确且更有目的性地显示体数据;自动视点选择方法则能快速地选取数据观察的最优位置,避免重要信息被遮挡,并获取尽量多的有效信息。
本文研究的重点包括:
1、在基于图像的转换函数设计的基础上,提出了基于粒子群优化和基于遗传粒子群算法的转换函数设计方法。该方法基于不同的转换函数评价方法分别实现了转换函数设计的自动和半自动化,在降低用户操作强度的同时,减少了设计次数,提高了设计效率。同时基于智能优化算法的粒子评价过程提出了一种基于主观评价和客观评价的混合评价方法。该方法在评价过程中综合考虑了用户的主观评价值和粒子的客观评价值,将其按照一定比例合成,得到最终评价值。这种评价方法可使可视化结果既满足用户的需求,又符合严谨的客观评价原则。
2、在简单转换函数设计的基础上,提出了一种复杂转换函数的设计方法。它把复杂转换函数设计问题转化为多个简单转换函数的融合设计问题。这种方法直观且易于实现,降低了复杂转换函数的设计难度。它把融合设计问题转化为对融合比例的参数寻优问题,采取基于预期适应度的相似性评价方法对融合比例作出评价,由PSO根据评价值生成新的融合比例,在很大程度上简化了复杂转换函数的设计。
3、在体绘制中提出了一种基于粒子群优化的自动视点选择方法,基于屏幕提供给用户包含最多数据信息的绘制图像。该方法根据视点信息量来评估视点质量,通过采用PSO迭代生成新视点,较大程度地减少了需评价的视点数目,从而消除了冗余的视点计算,在保证视点质量的同时提高了视点选择效率。
经过大量实验证明,本文提出的算法能较好地缩减用户可视化操作的工作量,有效提高了可视化效率,在体绘制的智能化方面做出了有益的探索。
Visualization of large datasets receives increasing attention from both engineering and medical communities in recent years.Direct Volume Rendering(DVR) has been proven to be an effective and important technique for visualizing 3D large-scale datasets.Compared with the traditional geometry rendering methods,it is more suitable for the visualization of scientific computation,since it exhibits information more accurately without losing any data.Traditional DVR methods heavily depend on users' experience to select the suitable transfer function(TF) and viewpoint,which makes it inefficient and hard to use.
This thesis studies automatic/semi-automatic methods for design of transfer functions and selection of optimal viewpoints based on intelligent optimization algorithms.The study on automatic design methods of transfer function aims to express the information inside volume datasets more accurately and more purposively. Automatic viewpoint selection is used to locate optimal viewpoints quickly which can avoid the occlusion of important data.
The main contributions of this thesis include:
1.Based on image-based TF design,a technique using Particle Swarm Optimization(PSO) and genetic PSO is presented to improve the efficiency of DVR.This method makes the transfer function design automatically,which is based on various ways of TF evaluation.It does not only ease users,but also reduces the time in adjustment.We also provide a mixed evaluation method for particle evaluation,which is based on both subjective and objective evaluations.According to this method,the final fitness value is formed of the subjective evaluation values from users and the objective evaluation values from several energy functions,on a given proportion.With this method,the visualization results can satisfy users,which follow objective principles precisely.
2.Based on the simple TF design,this thesis presents a technique for complicated TF design.It converts complicated TF design problem into the fusing problem of several simple TFs.The keystone is to formualte the TF fusing problem into searching for an optimal fusing proportion,by using a similarity evaluation method,which is based on expectation fitness.To a large extent,it simplifies the design process of complicated TF.
3.As for volume rendering,this thesis brings forward a PSO-based viewpoint selection method,which provides the viewpoint that can improve both the speed and efficiency of data understanding.During the process,it generates new viewpoints using PSO,and the quality of a viewpoint is intuitively related to how much information its corresponding view gives us about a scene.We use viewpoint entropy to define the informative view.This method remarkably reduces the number of viewpoint candidates,thus eliminates the reluctant viewpoint evaluations.Generally speaking,it improves the performance of the applications,and the viewpoint quality as well.
As proved with lots of experiments,these methods can greatly improve DVR efficiency,and ease the burden of users.Practice shows that we have done beneficial exploration for intellectualized visualization.
引文
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