Power图的离散生成

Power图是一种特殊的加权Voronoi图，该图中每个生成元点pi都带有权值wi．给出了一种直接构造Power图的算法．以每个生成元点Pi为圆心，Power距离,√wi为半径画圆；然后将这些圆以不同颜色填充，并以相同速率向外扩展这些圆的边界，直到屏幕上所有像素点都涂上颜色为止，环绕Pi的新边界构成Power图．该算法改进了在Voronoi图基础上构造Power图的传统方法，具有较高的效率．     

一种基于图的平面点集Delaunay三角剖分算法

本文提出了一种基于图的平面点集Ｄｅｌａｕｎａｙ三角剖分算法。该算法首先求出平面点集的欧几里得最小生成树，然后逐次加入一边构造三角形网格，最后按最小内角最大的三角化准则，通过局部变换，得到平面点集的Ｄｅｌａｕｎａｙ三角剖分。本文同时阐述了它的对偶图；平面点集的Ｖｏｒｏｎｏｉ图的概念和性质。     

基于容积约束Power图的图像分片逼近

给出一种在容积约束Power图结构上的图像分片多项式逼近方法.将Power图的权重与图像颜色信息相关联,设计了一种带容积约束Power图的顶点位置与权值交替优化的图像逼近算法.该算法运用误差反馈机制以及图像显著性检测等方法生成密度函数图像,并根据原始图像的颜色信息和得到的密度函数图像分两次来指导初始化点集生成,通过构建最终的Power图来逼近目标图像.利用Power图对目标图像进行区域分割,定义了度量逼近误差的带容积约束的优化能量函数,分别计算能量函数关于位置和权重的梯度,将原问题分解为两个子问题分而治之,借助密度函数图像生成的高效初始化点分布,通过不断更新Power图的顶点位置和权值得到相对较优的Power图,最终拟合出逼近图像.实验结果表明,该算法能够较好地逼近彩色图像,并有效保持了图像显著区域的特征.     

基于超图正则化非负Tucker分解的图像聚类算法

针对非负张量分解应用于图像聚类时忽略了高维数据内部几何结构的问题,在经典的张量非负Tucker分解的基础上,添加超图正则项以尽可能多地保留原始数据的内在几何结构信息,提出一种基于超图正则化非负Tucker分解模型HGNTD。通过构造超图刻画数据内部样本间的高阶关系,提高几何结构描述的准确性,针对超图正则化非负张量分解模型,基于交替非负最小二乘法,设计快速有效的超图正则化非负Tucker分解算法求解所给模型,证明算法在非负的条件下是收敛的,最终将算法应用于图像聚类。在Yale和COIL两个常用公开数据集上的实验结果表明,相对于k-means、非负矩阵分解、图正则化非负矩阵分解、非负Tucker分解和图正则化非负Tucker分解等算法,超图正则化非负Tucker分解算法聚类准确度提升了8.6%~11.4%,归一化互信息提升了2.0%~7.5%,具有更好的聚类效果。     

特征自表达和图正则化的鲁棒无监督特征选择

为了在揭示数据全局结构的同时保留其局部结构,本文将特征自表达和图正则化统一到同一框架中,给出了一种新的无监督特征选择(unsupervised feature selection,UFS)模型与方法。模型使用特征自表达,用其余特征线性表示每一个特征,以保持特征的局部结构;用基于 ${L_{2, 1}}$ 范数的图正则化项,在保留数据的局部几何结构的同时可以降低噪声数据对特征选择的影响;除此之外,在权重矩阵上施加了低秩约束,保留数据的全局结构。在6个不同的公开数据集上的实验表明,所给算法明显优于其他5个对比算法,表明了所提出的UFS框架的有效性。     

三角网格模型骨架提取算法

骨架图能够直观表达三维模型几何形状,很好地反映模型的拓扑特征,在工业机器人抓取、特征识别等领域有着广泛的应用。针对三角网格表达的工业零件给出一种骨架提取算法,该算法采用Reeb图对三角网格进行骨架的抽取运算。首先读取三角网格文件,并对复杂的三角网格进行简化处理,然后遍历所有的三角网格,采用Dijkstra算法抽取基本点集,根据定义的连续函数计算每个顶点的函数值,最后根据函数值得出模型的基本骨架。实验表明,该算法具有良好的计算效果和效率,提取出的骨架图较好地保存了三维模型拓扑结构和姿态,可作为后续研究三维模型搜索的特征描述符。     

基于平面直线图链的单调完全集的研究

计算几何中的链方法是平面剖分中一点定位的好方法，本文讨论了从任何平面直线图红正则化手续，进而构造链的单调完全集的方法和过程，这是用链方法进行一点定位的必要准备。     

基于图学习正则判别非负矩阵分解的人脸识别

基于图正则非负矩阵分解（NMF）算法充分利用了高维数据通常位于一个低维流形空间的假设从而构造拉普拉斯矩阵,但该算法的缺点是构造出的拉普拉斯矩阵是提前计算得到的,并没有在乘性更新过程中对它进行迭代。为了解决这个问题,结合子空间学习中的自表示方法生成表示系数,并进一步计算相似性矩阵从而得到拉普拉斯矩阵,而且在更新过程中对拉普拉斯矩阵进行迭代。另外,利用训练集的标签信息构造类别指示矩阵,并引入两个不同的正则项分别对该类别指示矩阵进行重构。该算法被称为图学习正则判别非负矩阵分解（GLDNMF）,并给出了相应的乘性更新规则和目标函数的收敛性证明。在两个标准数据集上的人脸识别实验结果显示,和现有典型算法相比,所提算法的人脸识别的准确率提升了1% ~ 5%,验证了其有效性。     

一个通用的快速三角化算法

提出了一个适用于任意平面多边形区域及散乱点集的通用三角化算法。当算法应用于多边形区域时,首先对各个顶点和区域内部的散乱点按扫描方式排序,然后依次扫描各点,扩展生成新的三角形,从而获得局部已剖分区域,并最终完成整个区域的三角化。将上述过程作适当改动后,可被用于平面散乱点集的三角网格化,该通用算法除了具有快速三角化的特点之外,还采用局部域的优化组合来体现最优化准则,因此算法更具有可操作性和实用性。     

右边正则纠删码中二部图的构造算法

通过深入研究右边正则度序列的分析性质,设计了右边正则纠删码度序列的参数优化算法.基于此算法,提出了右边正则纠删码设计中随机二部图的连边构造算法.数值结果证明了所给的度序列参数优化算法的有效性.仿真结果表明基于右边正则度序列的级联型纠删码的性能优于Tornado码.随机二部图的连边构造算法和度序列的参数优化算法有助于右边正则纠删码的设计及其工程应用.     

基于MapReduce的加权Voronoi图并行算法设计及应用

针对大规模数据的加权Voronoi图实现的复杂性和计算精度低问题, 采用欧氏距离法, 设计和实现了一种基于MapReduce编程模型的并行栅格加权Voronoi图的生成算法, 并将其成功应用于石家庄桥东区超市的推荐服务。该算法计算精度高, 同时可适用于任意点、线、面及复合发生元的加权Voronoi图的计算。实验结果表明, 算法在处理大规模栅格数据时能明显提高栅格Voronoi图的生成速度, 并能为用户推荐综合因素优选的超市。     

分区加权Voronoi图的性质及其面积计算

分区加权Voronoi图是Voronoi图和加权Voronoi图的推广,可以用来模拟移动通信中基站发射天线分扇区以不同功率向周围发射时所覆盖区域的形状。首先,给出了分区加权Voronoi图的性质、定理及相关证明;其次,分析了分区加权Voronoi图中的各种区域,并给出了一种计算相应区域面积的算法;最后,利用分区加权Voronoi图模拟石家庄市部分城区中的基站建设情况,并对模拟产生的重复覆盖、服务区和盲区面积进行了计算。     

变容量限制质心Power图的计算

Power 图作为 Voronoi 图的拓展,引入“权重”使其有着良好的限容特性。对普通 Power 图增加容 量约束,使得每个站点的容量等于预设的容量值,则可以得到容量限制 Power 图;在此基础上,再增加质心约 束,使每个站点刚好位于对应 Power 区域的质心,进一步得到质心容量限制 Power 图。在质心容量限制 Power 图中,容量限制条件均有明确的值,然而在某些应用中其往往是一个区间。针对区间容量限制问题,提出一种 变容量限制质心 Power 图的计算方法。一方面,该方法通过不断调整各站点的权重以使得站点的容量满足区间 限制;另一方面,Lloyd 方法被用于优化各站点的位置到对应 Power 区域的质心;两者交替迭代优化,从而得 到满足区间容量限制的质心 Power 图。在不同的密度和不同容量限制区间下的实验结果表明,该方法适用于不 同密度下变容量限制质心 Power 图的计算,并且具有高效、适应性强等优点。     

Three-dimensional beta shapes

The Voronoi diagram of a point set has been extensively used in various disciplines ever since it was first proposed. Its application realms have been even further extended to estimate the shape of point clouds when Edelsbrunner and Mücke introduced the concept of α-shape based on the Delaunay triangulation of a point set.In this paper, we present the theory of β-shape for a set of three-dimensional spheres as the generalization of the well-known α-shape for a set of points. The proposed β-shape fully accounts for the size differences among spheres and therefore it is more appropriate for the efficient and correct solution for applications in biological systems such as proteins.Once the Voronoi diagram of spheres is given, the corresponding β-shape can be efficiently constructed and various geometric computations on the sphere complex can be efficiently and correctly performed. It turns out that many important problems in biological systems such as proteins can be easily solved via the Voronoi diagram of atoms in proteins and β-shapes transformed from the Voronoi diagram.     

Dynamic polar diagram

The polar diagram [C.I. Grima, A. Márquez, L. Ortega, A new 2D tessellation for angle problems: The polar diagram, Computational Geometry 34 (2006) 58-74] of a set of points on the plane and the contracted dual of polar diagram (CDPD) [B. Sadeghi Bigham, A. Mohades, The dual of polar diagrams and its extraction, in: International Conference of Computational Methods in Sciences and Engineering ICCMSE, vol. 7, Greece, 2006, pp. 451-454] have been introduced recently. In this paper, we introduce the Dynamic Polar Diagram and present an algorithm to find it using CDPD and a hash structure for point location problem. In the dynamic polar diagram, the points can be added to or removed from the point set. For this problem, a brute-force method runs in O(nlogn) time and also there is a sketch of an algorithm in [C.I. Grima, A. Márquez, L. Ortega, A new 2D tessellation for angle problems: The polar diagram, Computational Geometry 34 (2006) 58-74] that takes O(n) time in all cases (best, average and worst). In our approach, we first determine an area out of which the polar diagram does not change due to insertion or deletion of a site. Then we present a new algorithm to solve the problem in O(kp) time where kp is the number of the sites whose polar regions are affected by the new addition or deletion of p.     

基于Voronoi图的方向区域查询方法

针对空间中方向区域查询效率不高的问题,通过引入Voronoi图,利用其特性对数据空间进行划分,提出了基于Voronoi图的方向区域查询方法.该方法在基于Delaunay三角网生成的Voronoi图索引结构基础上,将首结点与查询对象连线形成有向线段,利用Voronoi图可以通过邻接生成点延展的特点确定查询对象的位置,通过...     

An optimal algorithm for constructing the weighted voronoi diagram in the plane

Let S denote a set of n points in the plane such that each point p has assigned a positive weight w(p) which expresses its capability to influence its neighbourhood. In this sense, the weighted distance of an arbitrary point x from p is given by d_e(x,p)/w(p) where d_e denotes the Euclidean distance function. The weighted Voronoi diagram for S is a subdivision of the plane such that each point p in S is associated with a region consisting of all points x in the plane for which p is a weighted nearest point of S.An algorithm which constructs the weighted Voronoi diagram for S in O(n²) time is outlined in this paper. The method is optimal as the diagram can consist of Θ(n²) faces, edges and vertices.     

带权点集Laguerre图的增量算法与软件设计研究

Laguerre图作为Voronoi图的推广,在计算几何学、材料科学等领域中有着重要应用。重点讨论了带权点集Regular三角化的增量算法以及根据其对偶性质构造Laguerre图的实现过程;通过研究球填充带权点集对Laguerre图胞体结构特征的影响,在此基础上开发了用于参数化、自动化、可视化构造Laguerre图的软件;利用软件给出了多晶体材料与泡沫材料微结构仿真的应用实例,验证了软件的有效性。     

Partial shape-preserving splines

A complex geometric shape is often a composition of a set of simple ones, which may differ from each other in terms of their mathematical representations and the ways in which they are constructed. One of the necessary requirements in combining these simple shapes is that their original shapes can be preserved as much as possible. In this paper, a set of partial shape-preserving (PSP) spline basis functions is introduced to smoothly combine a collection of shape primitives with flexible blending range control. These spline basis functions can be considered as a kind of generalization of traditional B-spline basis functions, where the shape primitives used are control points or control polygons. The PSP-spline basis functions have all the advantages of the conventional B-spline technique in the sense that they are nonnegative, piecewise polynomial and of property of partition of unity. However, PSP-spline is a more powerful freeform geometric shape design technique in the sense that it is also a kind of shape-preserving spline. In addition, the PSP-spline technique implicitly integrates the weights of shape control primitives into its basis functions, which allows users to design a required geometric shape based on weighted control primitives. Though its basis functions are simply piecewise polynomial functions, it has the same shape design strengths as the rational piecewise polynomial based spline techniques such as NURBS. In particular, when control shape primitives are specified as a set of control points, PSP-spline behaves like a polygon smoother, with which a shape can be designed to approximate the specified control polygon or control mesh smoothly with any required precision. Consequently, a richer set of geometric shapes can be built using a relatively smaller set of control points.