B样条曲线曲面降阶综述

B样条曲线曲面的降阶是样条曲线和曲面造型中的关键技术之一,为了实现不同CAD系统之间的数据交换,常用到这一技术,它已成为热点问题,得到越来越多的研究.结合作者在该领域的研究成果,文章综述了近年来国内外专家学者关于B样条曲线曲面的降阶逼近研究的方法、理论成果及实际应用情况,并对各种不同的方法进行了分析比较.     

NURBS曲线曲面延伸

本文先介绍与曲线曲面延伸有关的非均匀有理Ｂ样条技术，然后研究ＮＵＲＢＳ曲线曲面的满足切平面连续和曲率连续的延伸的算法特别地详细推导曲率连续的数学表达式。     

均匀B样条曲线曲面的小波表示

小波基为曲线曲面带来了更为灵活的表达方式,均匀B样条曲线曲面在经过小波分解以后所得到的小波在定义域边界与内部可以采用统一的表达式,在进行小波重构时仅需作乘法运算,计算效率高。本文试图从几何概念出发由浅入深地论述基于小波的均匀三次B样条曲线曲面多分辨表示的原理及其实现。     

P-nary细分多层B样条算法及曲面拟合

多层B样条方法在曲面的拟合中提供了新的途径.在证明均匀B样条基函数P-nary细分方程基础上,给出了均匀B样条曲线的P-nary细分控制点之间的计算公式,进而讨论了B样条曲面的P-nary细分问题,并将其用于层次B样条曲面拟合.提出了基于P-nary细分多层B样条曲面拟合算法.该算法加快了层次B样条曲面拟合中网格加细后曲面控制点的计算,数值实例显示所给算法的有效性.     

与给定多边形相切的C 3连续B3样条曲线

描述了一种与给定多边形相切的B3样条曲线的算法。在算法中，所有的B3样条曲线的控制点可以通过对多边形的顶点简单计算产生，所构造的曲线对多边形具有保形性，曲线可以局部修改，最后给出了两个算例。     

局部构造C2连续的三次B样条插值曲线和双三次插值曲面

为了避免一般的局部插值算法生成的B样条曲线和曲面在段点处达不到理想的连续性以及出现多重内节点的问题,一种局部构造C2连续的三次B样条插值曲线和双三次插值曲面的方法被介绍。该方法借助节点插入算法逐步地迭代出样条控制顶点,其思想简单、几何直观、算法速度快,在曲线中夹直线段、尖点以及在曲面中夹棱边和平面都能比较容易实现。生成的曲线光滑度高、无重节点。文章最后还利用这种构造方法给出了一种在指定范围内按规定变形曲线的方法。     

LCD Mura缺陷的B样条曲面拟合背景抑制

针对机器视觉检测TFT-LCD Mura缺陷时存在的图像整体亮度不均匀、背景复杂等影响检测准确性的问题,提出一种基于B样条曲面拟合的背景抑制方法。在最小二乘法准则的约束下,采用双三次B样条曲面拟合算法拟合出背景,并添加光顺项调整拟合精度,用原始图像减去拟合背景,从而消除亮度不均匀背景对缺陷分割造成的影响。为提高算法速度,对原始图像进行分块拟合,并将双三次B样条函数分解为一元函数求解,减小了计算量,同时避免了对原函数求解时容易出现的病态解问题。实验结果表明,该算法准确、高效。     

有理B样条曲线的几何性质

本文讨论有理B样条曲线的几何性质,证明了有理B样条曲线具有包络性、保凸性和分割逼近性。     

区间Bezier曲线／曲面与Offset曲线／曲面之间的关系

本文证明了对任意已知的区间Ｂｅｚｉｅｒ曲线／曲面总存在两条／张Ｏｆｆｓｅｔ曲线／曲面分别包含与被包含此曲线／曲面，反之亦然。     

带有给定切线多边形的保形有理三次B样条曲线

本文给出了带有给定切线多边形的保形有理三次Ｂ样条曲线，其部分权因子可通过选取切点的位置来确定，由此方法还导出了保形有理三次Ｂ样条插值曲线，最后，给出了两个例子。     

提高混凝土无损测强曲线拟合精度的措施研究

研究了提高混凝土无损检测测强曲线拟合精度的方法.提出,在最小二乘法的基础上,选用合适的函数形式,采用分段拟合,以相对误差最小为目标,消除异常值的影响或采用人工神经网络等方法,提高测强曲线拟合的精度.     

曲线拟合在图像恢复中的应用

讨论了曲线拟合在图像恢复中的应用．对图像的各行信息进行了曲线拟合,从而消除加性背景干扰．使用该方法能使图像恢复出较均匀的像面．通过该处理能较好地消除X射线图像中的加性背景干扰．     

一个用圆弧逼近平面三次参数曲线的算法

本文给出一种用圆弧逼近平面三次Ｂ样条曲线的算法，该算法能保持曲线的整体光滑，达到Ｃ１连续．并可对逼近精度加以控制．     

参数三次B样条曲线的一种整体光顺方法

本文在能量法的基础上，提出了一种新的目标函数，给出了参数三次Ｂ样条曲线的一种新的整体光顺方法。利用这种方法得到的曲线不仅具有较小的应变能，而且曲率变化比较均匀，具有很好的光顺效果。该方法能推广到对曲面的光顺。     

Modelling the Pareto-optimal set using B-spline basis functions for continuous multi-objective optimization problems

In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.     

A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

Parametric curves such as Bézier and B-splines, originally developed for the design of automobile bodies, are now also used in image processing and computer vision. For example, reconstructing an object shape in an image, including different translations, scales, and orientations, can be performed using these parametric curves. For this, Bézier and B-spline curves can be generated using a point set that belongs to the outer boundary of the object. The resulting object shape can be used in computer vision fields, such as searching and segmentation methods and training machine learning algorithms. The prerequisite for reconstructing the shape with parametric curves is to obtain sequentially the points in the point set. In this study, a novel algorithm has been developed that sequentially obtains the pixel locations constituting the outer boundary of the object. The proposed algorithm, unlike the methods in the literature, is implemented using a filter containing weights and an outer circle surrounding the object. In a binary format image, the starting point of the tracing is determined using the outer circle, and the next tracing movement and the pixel to be labeled as the boundary point is found by the filter weights. Then, control points that define the curve shape are selected by reducing the number of sequential points. Thus, the Bézier and B-spline curve equations describing the shape are obtained using these points. In addition, different translations, scales, and rotations of the object shape are easily provided by changing the positions of the control points. It has also been shown that the missing part of the object can be completed thanks to the parametric curves.