基于双三次Bezier曲面在球体建模中的应用

传统球体建模的几何元素是由非参数表示的数学方程生成,建模过程繁琐,不易形变生成其他模型。针对这些不足,提出基于双三次Bezier曲面的球体建模方法。采用de Casteljau细分算法,反求圆和椭圆的双三次Bezier曲面的控制点,获得了影响Bezier曲面控制点的魔术常数,给出了球体双三次Bezier曲面的控制点坐标,实现了基于三次Bezier曲面的三维球体、椭球体网格模型的绘制。通过调整控制点参数,生成了类似蛋形体以及苹果体等曲面体网格模型。实验结果表明,魔术常数为计算旋转体模型的控制点提供了新的技术支持,Bezier方法进行三维建模具有很强的设计灵活性和实用性。     

基于粒子系统的物理约束植物根生长建模

提出了一种基于约束和粒子系统思想的植物根的建模方法，既体现了约束的微观控制和粒子系统的宏观控制的优点，又保证了模型实际应用的效率和速度，并结合Bezier曲线实现了图形显示的优化。     

基于Hough 变换的三次方Bezier 曲线检测算法研究

类似经典Hough 变换中对直线（段）、圆（弧）、椭圆、抛物线等解析曲 线的检测,论文研究了三次方Bezier 曲线的检测算法,提出了离散Bezier 曲线的特征建模 方法和使用R 函数的Hough 变换曲线检测快速算法。该算法能够根据所给出的待检测目标 点阵图像建立形状参数模型,然后检测该曲线在复杂图像中出现的位置、大小和方向。实验 表明,该法能够有效地检测任意三次方Bezier 曲线,且精确度优于目前广泛用于曲线检测 的广义Hough 变换。     

花卉植物形态与生长可视化仿真研究

花卉植物形态结构复杂,叶片、花瓣等组织器官的可视化建模和生长仿真成为虚拟花卉的研究瓶颈。基于花卉图像和植物生态学,提出一种通用的草本花卉形态结构建模和生长可视化仿真算法。结合花卉图像信息和双三次Bezier曲面模拟组织器官的形态,利用定义的花卉植物拓扑结构建立单株花卉。为了忠于植物生长特性,采用Logistic方程模拟叶片和花瓣的生长过程。通过郁金香、石竹、茉莉等多种代表性草本花卉植物验证了算法的有效性,模拟的花卉逼真自然。     

基于几何细节建模和双向纹理函数的真实感羽毛

根据羽毛具有近似规则的微观几何结构特点,提出了一种基于高精细几何细节建模和双向纹理函数的真实感羽毛建模和绘制算法。算法首先对羽毛进行基于Bezier或Hermite的参数化羽毛建模,进一步建立羽轴和羽枝的曲线管状体细节几何模型并离散网格化获得羽毛的高细节网格模型;最后利用该羽毛模型在不同光照和视线条件下采样的双向纹理函数（BTF）纹理和羽毛内在纹理合成的方法绘制基于BTF的真实感羽毛。     

环境动态模拟中的建模技术

环境的动态模拟涉及到的技术问题很多。该文结合奥运规划宣传片的制作,介绍了环境模拟中涉及到的建模技术。在环境模拟的建模中,涉及到房屋等规则几何体和植物等不规则几何体。规则几何体模型的建立可以直接使用三维设计软件提供的建摸工具．但是需要注意精简模型。植物等不规则模型的建立需要撮据植物生成的方法和三维设计软件的特点。结合具体应用的需要开发相应的制作工具.     

基于曲面三角形逼近的Bezier曲面表示研究

基于曲面三角形来逼近Bezier曲面,通过对曲面三角形的递归分割,可实现光滑的、多细节层次的实体模型.通过基于三角形链的优化,可实现Bezier曲面的快速绘制.该方法可应用于虚拟场景中的实体建模、地形模型的建模等领域.     

基于非均匀环境建模与三阶Bezier曲线的平滑路径规划

针对工作于复杂环境下的大型工装,本文提出了一种基于非均匀环境建模与三阶Bezier曲线的平滑路径规划算法,以指导工装的运动.在环境建模方面,利用四叉树建立环境的非均匀模型,能够有效压缩环境信息,提高搜索效率;在路径搜索方面,以非均匀环境模型为基础,提出一种距离启发搜索和信息素混合更新的蚁群算法,能够得到工装的安全可行路径点;在路径平滑方面,基于三阶Bezier曲线,提出能够连接任意位置和任意方向两点的转弯单元的设计方法,利用转弯单元连接路径搜索算法得到的路径点,能够获得满足工装非完整性约束的平滑路径.最后,以大型激光驱动器的靶场环境为对象,对本文算法的有效性和可靠性进行验证,并利用DELMIA平台进一步验证了规划路径的运动平滑性和安全性.     

Bezier曲线间最近距离的计算方法

针对Bezier曲线间最近距离计算问题,提出一种简捷、可靠的计算方法．该方法以Bernstein多项式算术运算为工具,建立Bezier曲线间最近距离的计算模型;然后充分利用Bezier曲面的凸包性质和de Casteljau分割算法进行求解．该方法几何意义明确,能有效地避免迭代初始值的选择和非线性方程组的求解,并可进一步推广应用于计算Bezier曲线／曲面间的最近距离．实验结果表明,该方法简捷、可靠且容易实现,与Newton-Raphson方法的融合可进一步提高该方法的运行速度．     

一种通用的植物逼真几何建模方法

针对使用 L 系统进行植物几何建模的具体过程随规则定义的变化而变化的问题 ,提出了一种较为通用的基于 L 系统规则语言分析器的解决方法 ,即通过归纳和抽象得到可以定义多种 L 系统规则的语言 L- plants,并为其构造语言分析器 ,完成 L 系统开始状态和规则的识别 ,进行规则替换 ,以形成最终的字符串 ,最后使用形状语法对字符串进行解释 ,建立出植物的几何模型 .实验证明 ,该方法可以较大幅度地提高植物几何建模的效率     

Polynomial surfaces interpolating arbitrary triangulations

Triangular Bezier patches are an important tool for defining smooth surfaces over arbitrary triangular meshes. The previously introduced 4-split method interpolates the vertices of a 2-manifold triangle mesh by a set of tangent plane continuous triangular Bezier patches of degree five. The resulting surface has an explicit closed form representation and is defined locally. In this paper, we introduce a new method for visually smooth interpolation of arbitrary triangle meshes based on a regular 4-split of the domain triangles. Ensuring tangent plane continuity of the surface is not enough for producing an overall fair shape. Interpolation of irregular control-polygons, be that in 1D or in 2D, often yields unwanted undulations. Note that this undulation problem is not particular to parametric interpolation, but also occurs with interpolatory subdivision surfaces. Our new method avoids unwanted undulations by relaxing the constraint of the first derivatives at the input mesh vertices: The tangent directions of the boundary curves at the mesh vertices are now completely free. Irregular triangulations can be handled much better in the sense that unwanted undulations due to flat triangles in the mesh are now avoided.     

由深度数据重建三维物体的一种方法

讨论由深度数据重建三维物体的一种新方法.其基本思想是根据由基于物体旋转的主动 式三角法三维信息获取系统所获取的物体深度数据作为该物体的型值点.再根据型值点进行 网格划分,以便形成满足一定约束条件的空间拓扑网格.最后使用有理Bezier三角曲面片与 矩形曲面片相结合的方法进行曲面拟合与逼近.     

An agent-monitored framework for the output-oriented design of experiments in exploratory modelling

Exploratory modelling is an approach for modelling under uncertainty based on the generation and analysis of computational experiments. The results of exploratory modelling are sensitive to the way that experiments are designed, such as the way that the uncertainty space is delineated. This article introduces an agent-monitored framework—i.e. a design metaphor of the interactions among modellers and stakeholders and the simulation process—for controlling the design of experiments based on monitoring model behaviour in the output space. To demonstrate the benefits of the suggested framework in the exploratory modelling process, the article shows how the use of the framework with an output-oriented approach informs the delineation of an appropriate uncertainty space with an illustrative example in the decision-making context. The article concludes that the design of experiments based on feedback from the output space can be a useful approach: to control simulations in exploratory modelling; to build more confidence in final results; and to inform the design of other aspects of experiments, such as selecting policy levers and sampling method.     

G2 quasi-developable Bezier surface interpolation of two space curves

Surface development is used in many manufacturing planning operations, e.g., for garments, ships and automobiles. However, most freeform surfaces used in design are not developable, and therefore the developed patterns are not isometric to the original design surface. In some domains, the CAD model is created by interpolating two given space curves. In this paper, we propose a method to obtain a G² quasi-developable Bezier surface interpolating two arbitrary space curves. The given curves are first split into a number of piecewise Bezier curves and elemental Bezier patches each of which passes through four splitting points are constructed. All neighboring elemental patches are G² connected and they are assembled optimally in terms of the degree of developability (the integral Gaussian curvature). Experiments show that the final composite Bezier surface is superior to a lofted one which is defined regardless of the final surface developability.     

Semantic spaces for improving language modeling

Language models are crucial for many tasks in NLP (Natural Language Processing) and n-grams are the best way to build them. Huge effort is being invested in improving n-gram language models. By introducing external information (morphology, syntax, partitioning into documents, etc.) into the models a significant improvement can be achieved. The models can however be improved with no external information and smoothing is an excellent example of such an improvement.In this article we show another way of improving the models that also requires no external information. We examine patterns that can be found in large corpora by building semantic spaces (HAL, COALS, BEAGLE and others described in this article). These semantic spaces have never been tested in language modeling before. Our method uses semantic spaces and clustering to build classes for a class-based language model. The class-based model is then coupled with a standard n-gram model to create a very effective language model.Our experiments show that our models reduce the perplexity and improve the accuracy of n-gram language models with no external information added. Training of our models is fully unsupervised. Our models are very effective for inflectional languages, which are particularly hard to model. We show results for five different semantic spaces with different settings and different number of classes. The perplexity tests are accompanied with machine translation tests that prove the ability of proposed models to improve performance of a real-world application.     

Bezier曲Bezier曲线修改的一种分割算法线修改的一种分割算法

在图形图像处理过程中,Bezier曲线的应用是非常广的.通过增加控制点对Bezier曲线作修改,为了不改变原有曲线的形状,＂升阶＂是最常用的方法.本文从另一角度出发,提出把Bezier曲线先分割为两段曲线,无需对曲线进行＂升阶＂,只需重新计算控制点,从而也达到对曲线修改的目的,并且以三次Bezier曲线为例,给出了具体的求解过程.实践证明明,这种方法是可行的.     

Bezier分形曲线的细分叠加生成方法

给出了一种由Bezier曲线生成分形曲线的细分叠加方法。将参数二周期化后的Bezier曲线进行递归细分,得到细分曲线序列,再依次将此细分曲线序列无限叠加,构造出处处连续而处处不可微的分形曲线,具有某种自相似性。此Bezier分形曲线可表示为原Bezier曲线控制顶点的线性组合,其调配函数由参数二周期化后的Bernstein基函数无限细分叠加生成,处处连续而处处不可微,且有某种自相似性。数值实验表明此细分叠加方法所生成的曲线具有分形特征。     

A Multiobjective Particle Swarm Optimization-Based Partial Classification for Accident Severity Analysis

Reducing accident severity is an effective way to improve road safety. In this article, a novel multiobjective particle swarm optimization (MOPSO)-based partial classification method is employed to identify the contributing factors that impact accident severity. The accident dataset contains only a few fatal accidents but the patterns of fatal accidents are of great interest to traffic agencies. Partial classification can deal with the unbalanced dataset by producing rules for each class. The rules can be evaluated by several conflicting criteria such as accuracy and comprehensibility. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule-learning algorithms. The results show that the proposed approach can generate a set of accurate and comprehensible rules, which can indicate the relationship between risk factors and accident severity.     

基于迭代方法的多层Markov网络信息检索模型

查询扩展是提高检索效果的有效方法,传统的查询扩展方法大都以单个查询词的相关性来扩展查询词,没有充分考虑词项之间、文档之间以及查询之间的相关性,使得扩展效果不佳。针对此问题,该文首先通过分别构造词项子空间和文档子空间的Markov网络,用于提取出最大词团和最大文档团,然后根据词团与文档团的映射关系将词团分为文档依赖和非文档依赖词团,并构建基于文档团依赖的Markov网络检索模型做初次检索,从返回的检索结果集合中构造出查询子空间的Markov网络,用于提取出最大查询团,最后,采用迭代的方法计算文档与查询的相关概率,并构建出最终的基于迭代方法的多层Markov网络信息检索模型。实验结果表明 该文的模型能较好地提高检索效果。