扰动约束和最佳平方逼近的B样条曲线的降阶

将扰动约束技术应用于B啨ｚｉｅｒ曲线的降阶给出了理想的结果 讨论了将这类方法应用于B样条曲线降阶时结果不理想的原因 ,提出了采用最佳平方逼近技术对B样条曲线做降阶运算的方法 ;并用实例对该方法和基于扰动约束的降阶方法进行了比较     

B样条曲线降阶新方法

首先导出了 B样条曲线退化的条件 ,然后根据 B样条升阶恒等式提出了 B样条曲线降阶的新算法 .最后 ,对结果进行了简要的误差分析 .如果结合节点插入技术 ,还可以将降阶后的误差限定在给定的容差之内 .实践表明 ,该算法容易实现、效率高、逼近效果好 .     

B样条曲线最小二乘降阶方法

提出一种新的B样条曲线降阶方法.该方法利用B样务基转换矩阵建立B样条曲线降阶的数学模型,将B样条曲线的降阶问题转化为求线性方程组的最小二乘解问题.该方法基于整体考虑不必对B样条曲线分段处理,步骤简单易实现;可一次降多阶,避免了重复一次降一阶运算引起的误差累积,而当仅降一阶时与基于控制顶点扰动的约束优化降阶方法的逼近效果一致;在降阶的同时可满足各种给定的端点约束条件,以满足实际应用中的各种要求.     

均匀B样条曲线的降阶

文中给出均匀 B样条曲线退化的充要条件 ,证明了满足退化条件的 k阶均匀 B样条曲线可以表示为 (k- 1)阶均匀 B样条曲线 ,并给出相应的表示方法 .在此基础上 ,利用约束优化方法 ,提出均匀 B样条曲线的一种降阶方法 .而传统方法从升阶的反问题考虑降阶 ,要求被降阶的 B样条曲线的所有节点都是多重节点 ,从而无法处理均匀B样条曲线 .     

L∞范数下使用基本曲线和修正曲线的带约束Bézier曲线降阶

为避免直接求解基于L∞距离的带约束逼近的非线性最优解引起的复杂性,提出了一种把降阶逼近曲线分解为基本曲线和修正曲线的降阶方法.基本曲线利用约束Legendre多项式可得到显式解,且保证降阶后曲线满足要求的边界插值条件;修正曲线的控制顶点由降阶逼近曲线和原曲线的差定义,能够在L∞范数意义下极小化降阶逼近曲线与原曲线的误差.文中方法以简单稳定的方式实现保端点插值的一次降多阶,并达到L∞范数意义下对原曲线的近似最佳逼近.最后通过实例说明了文中方法的有效性.     

基于广义逆节点消去的B样条曲线的可控逼近

提出了一个基于节点消去的B样条曲线的逼近算法。该算法首先从插值于给定数据点的一阶B样条曲线出发,利用广义逆矩阵实现节点消去,并通过升阶、最小二乘逼近和投影修正误差等步骤,得到了与给定数据点的误差在容许范围内的逼近曲线。     

基于节点矢量优化的复合材料序列轮廓逼近及重构

针对采用边缘检测方法获得的复合材料第二相颗粒轮廓不规则且光顺性差的特点,利用周期非均匀三次B样条曲线逼近颗粒轮廓.将节点作为变量,提出了一种基于群体增量学习算法的节点矢量优化算法,得到预定控制点条件下误差最小的颗粒轮廓曲线;基于逼近的序列轮廓,采用柔性间距插入节点的方法定义公共节点矢量,根据公共节点矢量对序列轮廓进行相容性处理,利用三次B样条蒙皮算法生成了一张C2连续的张量积B样条曲面来描述复合材料的第二相颗粒.实验结果表明,与遗传算法相比,文中算法在轮廓逼近方面具有更好的鲁棒性和更高的精度,重构出的第二相颗粒光顺性好,效果理想.     

B样条曲线的降阶公式及近似降阶方法

已有的B样条曲线降阶方法，由于无降阶公式可循，对于可降阶曲线常要通过解一系列线性方程组来实现降阶．该文给出了B样条曲线的降阶公式，使得可直接用降阶公式对可降阶曲线进行降阶．利用降阶公式和约束优化方法，文中进一步给出了B样条曲线的一种近似降阶方法和相应的算法，使得在用约束优化方法求出可降阶的近似曲线后，就可直接用降阶公式求出降阶曲线，简化了降阶过程．该方法应用范围广且简单实用．     

区间B样条曲线的降阶

提出了区间B样条曲线的降阶问题,并给出了两种不同的降阶方法:整体降阶法与分段降阶法.给出了两个实例来说明算法,并比较了两种算法的优劣.实验结果表明,无论对均匀区间B样条曲线还是非均匀区间B样条曲线,分段降阶方法比整体降阶方法都更有效,而且能得到更紧的降阶区间B样条曲线.     

B样条曲线同时插入多个节点的快速算法

基于离散B样条的一个新的递推公式，提出B样条曲线同时插入多个节点的新算法。不同于Cohen等插入节点的Oslo算法，本算法用新的方法离算离散B样条，求每个离散B样条的值只需O(1)的运算量，从而使本算法高效，其时间复杂性为O(sk n)，其中k为B样条曲线的阶，n k 1为原节点数，s为新插入节点的个数,本算法的通用性强，适用于端点插值的和非端点插值的B样条曲线，可同时在曲线定义域内外的任意位置上插入任意个节点。     

Efficient planar object tracking and parameter estimation usingcompactly represented cubic B-spline curves

In this paper, we consider the problem of matching 2D planar object curves from a database, and tracking moving object curves through an image sequence. The first part of the paper describes a curve data compression method using B-spline curve approximation. We present a new constrained active B-spline curve model based on the minimum mean square error (MMSE) criterion, and an iterative algorithm for selecting the “best” segment border points for each B-spline curve. The second part of the paper describes a method for simultaneous object tracking and affine parameter estimation using the approximate curves and profiles. We propose a novel B-spline point assignment algorithm which incorporates the significant corners for interpolating corresponding points on the two curves to be compared. A gradient-based algorithm is presented for simultaneously tracking object curves, and estimating the associated translation, rotation and scaling parameters. The performance of each proposed method is evaluated using still images and image sequences containing simple objects     

Reducing control points in lofted B-spline surface interpolation using common knot vector determination

A new algorithm for reducing control points in lofted surface interpolation to rows of data points is presented in this paper. The key step of surface lofting is to obtain a set of compatible B-spline curves interpolating each row. Given a set of points and their parameterization, a necessary and sufficient condition is proposed to determine the existence of interpolating B-spline curves defined on a given knot vector. Based on this condition, we first properly construct a common knot vector that guarantees the existence of interpolating B-spline curves to each row of points. Then we calculate a set of interpolating B-spline curves defined on the common knot vector by energy minimization. Using this method, fewer control points are employed while maintaining a visually pleasing shape of the lofted surface. Several experimental results demonstrate the usability and quality of the proposed method.     

The continuous non-linear approximation of procedurally defined curves using integral B-splines

This paper outlines an algorithm for the continuous non-linear approximation of procedurally defined curves. Unlike conventional approximation methods using the discrete L_2 form metric with sampling points, this algorithm uses the continuous L_2 form metric based on minimizing the integral of the least square error metric between the original and approximate curves. Expressions for the optimality criteria are derived based on exact B-spline integration. Although numerical integration may be necessary for some complicated curves, the use of numerical integration is minimized by a priori explicit evaluations. Plane or space curves with high curvatures and/or discontinuities can also be handled by means of an adaptive knot placement strategy. It has been found that the proposed scheme is more efficient and accurate compared to currently existing interpolation and approximation methods.     

Smooth surface approximation to serial cross-sections

The reconstruction of the surface model of an object from 2D cross-sections plays an important role in many applications. In this paper, we present a method for surface approximation to a given set of 2D contours. The resulting surface is represented by a bicubic closed B-spline surface with C² continuity. The method performs the skinning of intermediate contour curves represented by cubic B-spline curves on a common knot vector, each of which is fitted to its contour points within a given accuracy. In order to acquire more compact representation for the surface, the method includes an algorithm for reducing the number of knots in the common knot vector. The proposed method provides a smooth and accurate surface model, yet realizes efficient data reduction. Some experimental results are given using synthetic and MRI data.     

用LS-SVMs整体构造B样条曲线

由给定的空间数据点集构造B样条曲线是CAGD中一个重要研究课题,常用的逼近方法实质上是基于“经验风险”意义下的最小二乘逼近。文章讨论了基于“结构风险”意义下用最小二乘支持向量回归机整体构造B样条曲线的逼近问题,其出发点是最小化结构风险,而不是传统学习的经验风险最小化,从而在理论上保证了好的推广能力,能够实现对原始曲线的逼近而不仅仅是对测量数据点的逼近。文章建立了B样条曲线拟合的数学模型,并构造了一种特殊的核函数来保证曲线的B样条表示形式。该方法为曲线拟合问题提供了新思路,数值实验证实了可行性。     

基于双正交非均匀B样条小波的曲线逼近方法

针对B样条曲线逼近有序数据点在应用最小二乘法时出现的计算量较大问题,提出一种基于双正交非均匀B样条小波的曲线逼近方法。其基本思想是：先用最小二乘法生成初始B样条逼近曲线,再用细节曲线逼近误差向量,接着将细节曲线叠加于原逼近曲线得到新的B样条曲线,这个过程是迭代的。细节曲线的基函数是双正交非均匀B样条小波。与传统最小二乘法相比,该方法仅需计算新增线性系统,避免重复计算原系统,降低了计算量,提高了运算效率;此外,给出了B样条逼近曲线的一种多分辨率表示形式。     

Low-degree approximation of high-degree B-spline surfaces

In this paper, a method for approximate conversion of high degree Bezier and B-spline surfaces to lower degree representations is presented to facilitate the exchange of surface geometry between different geometric modeling systems. Building on previous work on curve approximation, the method uses adaptive sampling to compute approximation error and lofting of isoparametric curves to produce the approximating surface. In addition, a bound for the approximation accuracy is computed using convex hulls.     

基于二进制GA的B样条重构曲线节点优化

带法向约束的自由曲线曲面重构在光学反射面设计中起至关重要的作用.本文为解决法向约束下的曲线重构问题提出了一种优化方案,使得重构出的曲线在逼近数据点的同时,亦能满足相应法向约束.首先,利用惩罚函数的方法将带法向约束的优化问题转化为无约束的优化问题.然后,引入二进制编码的遗传算法（GA）,建立合适的适应度函数,自适应产生优化节点向量,如此迭代进化,直到产生令人满意的重构曲线为止.考虑到节点向量非递减的特性,而遗传算法在寻找最优节点向量的过程中有可能打乱节点向量的顺序,所以在建立适应度函数的时候将变量调整为无序有界变量.通过与传统最小二乘方法和粒子群智能优化方法的比较,本文方案在解决带法向条件约束的曲线重构问题上优势明显,且对于任意形状的曲线重构都行之有效.