隐式多项式曲线的信息相似性分析研究

隐式多项式曲线用来描述数据点集合轮廓具有良好的性质,并已经获得广泛的应用。研究一般商业数据的特点,将一维商业数据变换成二维商业数据,使用隐式多项式曲线对其进行拟合描述,计算这些隐式多项式曲线的相似性,进而实现对商业数据的相似性计算分析。实验结果证明了算法的有效性。     

隐式多项式曲线数据拟合的稳定性分析

对数据点集合轮廓进行隐式多项式曲线或者曲面建模需要分析隐式多项式曲线的稳定性,因为这直接关系到隐式多项式曲线或者曲面建模是否有效。通过对隐式多项式曲线上点和系数关系的分析,从理论上得出了隐式多项式曲线上点的变动对其最高次齐次项影响很小,而数据点距离原点越远则隐式多项式曲线建模的有效性越差。文章的实验显示了隐式多项式曲线建模的数据点预处理方法和应用依据。     

隐式B样条曲线拟合的加权PIA算法

为了使拟合数据点的曲线生成速度更快、误差更小,提出一种隐式B样条曲线拟合数据点的加权PIA算法.首先,用待拟合数据点以及给定法向量生成偏移点集.然后,通过偏移点集构造差分向量,从而得到需要调整的误差控制系数,为了使迭代效率更高,在迭代过程中对误差控制系数做加权处理.最后,用最新的控制系数矩阵得到拟合数据点的曲线.文中5个数值算例采用均匀节点序列,实验结果表明,在相同迭代次数下,相对于I-PIA算法,该算法得到的拟合曲线误差值更小,曲线能更好保特征.     

采用轮廓特征的烟叶部位组分类研究

研究了烟叶轮廓曲线的二元正交多项式拟合算法以及拟合多项式在烟叶部位分组中的应用。发现用二元正交多项式曲线能较好地描述烟叶边缘,方便计算烟叶的长、宽、周长、面积等形状特征值,而且该多项式还具有平移、旋转和尺度不变性等特征。研究结果表明,利用该研究提出的多项式拟合系数进行烟叶部位组的判别分类效果良好,正确区分率达到92%以上,此方法可为进一步研究烟叶收购质量的自动分级提供可行的技术手段。     

基于BP神经网络的隐式曲线构造方法

隐式曲线与曲面是当前计算机图形学研究的热点之一。通过把BP神经网络与隐式曲线构造原理相结合，提出了一种构造隐式曲线的新方法，即首先由约束点构造神经网络的输入与输出，把描述物体边界曲线的隐式函数转化为显式函数；然后用BP神经网络对此显式函数进行逼近；最后由仿真曲面得到物体边界的拟合曲线。该新方法不同于传统的对显式函数的逼近方法，因为传统方法无法描述封闭的曲线；也不同于基于优化的拟合隐式曲线方法，因为它无须考虑函数的形式或多项式的次数。实验表明，该新方法有很强的物体边界描述能力和缺损修复能力，因而在物体边界重建、缺损图像复原等领域有一定的应用前景。     

关于范围查询的动态轮廓计算方法

考虑关于范围查询的动态轮廓计算问题。基于范围查询将数据集中的点利用点与查询之间的距离特性进行转换,转换后的点可视为是一个以查询点为坐标原点的新空间中的数据点,利用现有的轮廓计算方法对转换后的数据集进行计算得到轮廓集合,以此为基础设计相应的算法来实现针对范围查询的动态轮廓的计算。利用网格索引和Z曲线结构设计多个剪枝策略降低算法的空间与时间复杂度。实验结果验证了算法的有效性以及在时间和空间上的效率。     

带法向约束的隐式B样条曲线重构PIA方法EI北大核心CSCD

为使隐式曲线能够更好地拟合散乱数据点及其几何特征,提出一种带法向约束的隐式曲线重构渐进迭代(progressive and iterative approximation,PIA)方法.首先,基于隐式B样条函数提出有效的曲线拟合模型;其次,通过加入偏移数据点来消除额外零水平集,同时加入法向项来控制曲线的法向误差;最后,经多次优化迭代得到高精度的拟合曲线.在配置为2.6 GHz英特尔处理器,内存为16 GB的电脑上采用MATLAB实现编程.经多条不同形态封闭曲线拟合的实验结果表明,与隐式PIA(implicit PIA,I-PIA)方法和T样条曲线重构方法相比,从数据点精度和法向误差以及收敛速度3个评价指标进行评估,该方法能够在保证数据点精度的前提下,有效地降低法向误差,并具有更快的收敛速度.此外,实例结果也表明该方法具备鲁棒性.     

有理曲线的均匀区间隐式化

参数式曲线与隐式曲线是CAGD中常用的两种曲线形式,因此需要建立起二者之间相互转换的体制.长期以来,许多工作都集中在利用结式思想,将一个参数式曲线精确转化为一个隐式曲线上,而事实上用隐式曲线精确表示一条参数式曲线不仅非常麻烦,而且往往也没有必要.故此提出了参数式有理曲线均匀区间隐式化的一种新方法,利用区间算术和空间重心坐标的定义,可以用一个低阶区间多项式隐式曲线来逼近所给的参数式有理曲线,同时使一些目标函数最小化,达到用隐式多项式曲线来逼近参数式有理曲线的很好效果,并提供了一些算法和实例.     

单幅人体切片图像的多轮廓二维重构算法研究与实现

基于切片图像数据的轮廓曲线二维重构是轮廓表面三维重构的基础。单幅切片图像可能存在有单轮廓或多轮廓。本文对中国虚拟人切片图像进行分析,针对单幅切片图像里的多轮廓线情况,研究提出了多轮廓提取算法和拟合曲线建模算法。经编程实验,成功实现了单幅图像里的多轮廓二维重构。     

基于广义多项式神经网络的点云数据隐式曲面重构方法

针对点云数据的三维重建问题,提出了一种隐曲面重构的广义多项式神经网络新方法.该广义多项式神经网络隐层各神经元激励函数互不相同且线性无关,能够对应地学习点云数据样本中不同的模式,因此,具有较好的学习能力.基于梯度下降法原理,推导了其学习算法.仿真实验尝试将该方法应用于一些简单封闭物体的带噪点云数据隐式曲面重建,取得了较理想的重建质量和去噪效果.     

隐含多项式曲线曲面拟合次数的确定研究

选用合适次数的隐含多项式曲线曲面描述目标物体是处理和识别目标物体的关键,因而需要在理论上解决隐含多项式曲线或者曲面的次数确定问题.根据目标物体本身的特征,从理论上得出隐含多项式曲线描述物体的次数确定定理,并给出了具体计算公式.该方法首先由给定物体边界的轮廓检测出其驻点数,然后根据驻点数得到拟合隐含多项式曲线方程次数的下界,进而推广到三维物体的隐含多项式曲面拟合次数的确定.最后给出的应用实例进一步验证了算法的有效性与可操作性.     

Implicit Fitting Using Radial Basis Functions with Ellipsoid Constraint

Implicit planar curve and surface fitting to a set of scattered points plays an important role in solving a wide variety of problems occurring in computer graphics modelling, computer graphics animation, and computer assisted surgery. The fitted implicit surfaces can be either algebraic or non‐algebraic. The main problem with most algebraic surface fitting algorithms is that the surface fitted to a given data set is often unbounded, multiple sheeted, and disconnected when a high degree polynomial is used, whereas a low degree polynomial is too simple to represent general shapes. Recently, there has been increasing interest in non‐algebraic implicit surface fitting. In these techniques, one popular way of representing an implicit surface has been the use of radial basis functions. This type of implicit surface can represent various shapes to a high level of accuracy. In this paper, we present an implicit surface fitting algorithm using radial basis functions with an ellipsoid constraint. This method does not need to build interior and exterior layers for the given data set or to use information on surface normal but still can fit the data accurately. Furthermore, the fitted shape can still capture the main features of the object when the data sets are extremely sparse. The algorithm involves solving a simple general eigen‐system and a computation of the inverse or psedo‐inverse of a matrix, which is straightforward to implement.     

The 3L algorithm for fitting implicit polynomial curves andsurfaces to data

We introduce a completely new approach to fitting implicit polynomial geometric shape models to data and to studying these polynomials. The power of these models is in their ability to represent nonstar complex shapes in two(2D) and three-dimensional (3D) data to permit fast, repeatable fitting to unorganized data which may not be uniformly sampled and which may contain gaps, to permit position-invariant shape recognition based on new complete sets of Euclidean and affine invariants and to permit fast, stable single-computation pose estimation. The algorithm represents a significant advancement of implicit polynomial technology for four important reasons. First, it is orders of magnitude taster than existing fitting methods for implicit polynomial 2D curves and 3D surfaces, and the algorithms for 2D and 3D are essentially the same. Second, it has significantly better repeatability, numerical stability, and robustness than current methods in dealing with noisy, deformed, or missing data. Third, it can easily fit polynomials of high, such as 14th or 16th, degree. Fourth, additional linear constraints can be easily incorporated into the fitting process, and general linear vector space concepts apply     

Stable fitting of 2D curves and 3D surfaces by implicit polynomials

This work deals with fitting 2D and 3D implicit polynomials (IPs) to 2D curves and 3D surfaces, respectively. The zero-set of the polynomial is determined by the IP coefficients and describes the data. The polynomial fitting algorithms proposed in this paper aim at reducing the sensitivity of the polynomial to coefficient errors. Errors in coefficient values may be the result of numerical calculations, when solving the fitting problem or due to coefficient quantization. It is demonstrated that the effect of reducing this sensitivity also improves the fitting tightness and stability of the proposed two algorithms in fitting noisy data, as compared to existing algorithms like the well-known 3L and gradient-one algorithms. The development of the proposed algorithms is based on an analysis of the sensitivity of the zero-set to small coefficient changes and on minimizing a bound on the maximal error for one algorithm and minimizing the error variance for the second. Simulation results show that the proposed algorithms provide a significant reduction in fitting errors, particularly when fitting noisy data of complex shapes with high order polynomials, as compared to the performance obtained by the above mentioned existing algorithms.     

Describing complicated objects by implicit polynomials

This paper introduces and focuses on two problems. First is the representation power of closed implicit polynomials of modest degree for curves in 2-D images and surfaces in 3-D range data. Super quadrics are a small subset of object boundaries that are well fitted by these polynomials. The second problem is the stable computationally efficient fitting of noisy data by closed implicit polynomial curves and surfaces. The attractive features of these polynomials for Vision is discussed     

多结点样条插值及其多尺度细化算法

针对风线与曲面拟事问题，研究多结点样条插值方法。这类方法具有基数型，显式计算及局部性等优点。主要的新结果是：对多结点样条基本函数的构造给出了新的表述；提出了一类新的不带移动的混合形多结点样条基本函数；基于多尺度分析的思想，给出了一种自适应的细化算法，它对消减采样数据的相关性是简便有效的。     

隐含多项式曲线对称性几何特征及其检测研究

隐舍多项式曲线在物体描述和识别中具有许多优点并得到实际应用,因而物体的对称性检测问题可以转换成对隐含多项式曲线的对称性检测来研究。对隐含多项式曲线对称几何结构性质进行了探讨,提出隐含多项式曲线如果是对称的,则其充分必要条件是首二次因子积组成的椭圆图形是对称的,同时指出椭圆图形对称轴就是隐含多项式曲线的对称轴。算法较为简单和直观．实验结果证明算法的有效性和可操作性。     

隐多项式曲线的快速逐点生成算法

隐多项式曲线一直没有理想的生成算法,给出了一种针对二维n次隐多项式曲线的快速逐点生成算法,该算法思路简洁,在逐点生成过程中,只用到整数加减法,故速度快,效率高,具有广泛的应用价值。最后,运用算法给出了曲线生成实例和对算法效率的比较,比较结果表明本文提出的算法有效的提高了生成曲线的效率。