Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

Suboptimal Robot Joint Interpolation Within User-Specified Knot Tolerances

Approximation of a desired robot path can be accomplished by interpolating a curve through a sequence of joint-space knots. A smooth interpolated trajectory can be realized by using trigonometric splines. But, sometimes the joint trajectory is not required to exactly pass through the given knots. The knots may rather be centers of tolerances near which the trajectory is required to pass. In this article, we optimize trigonometric splines through a given set of knots subject to user-specified knot tolerances. The contribution of this article is the straightforward way in which intermediate constraints (i.e., knot angles) are incorporated into the parameter optimization problem. Another contribution is the exploitation of the decoupled nature of trigonometric splines to reduce the computational expense of the problem. The additional freedom of varying the knot angles results in a lower objective function and a higher computational expense compared to the case in which the knot angles are constrained to exact values. The specific objective functions considered are minimum jerk and minimum torque. In the minimum jerk case, the optimization problem reduces to a quadratic programming problem. Simulation results for a two-link manipulator are presented to support the results of this article.     

Knot calculation for spline fitting via sparse optimization

Curve fitting with splines is a fundamental problem in computer-aided design and engineering. However, how to choose the number of knots and how to place the knots in spline fitting remain a difficult issue. This paper presents a framework for computing knots (including the number and positions) in curve fitting based on a sparse optimization model. The framework consists of two steps: first, from a dense initial knot vector, a set of active knots is selected at which certain order derivative of the spline is discontinuous by solving a sparse optimization problem; second, we further remove redundant knots and adjust the positions of active knots to obtain the final knot vector. Our experiments show that the approximation spline curve obtained by our approach has less number of knots compared to existing methods. Particularly, when the data points are sampled dense enough from a spline, our algorithm can recover the ground truth knot vector and reproduce the spline.     

平面三次NURBS曲线的自动光顺算法

针对平面三次NURBS曲线的光顺问题，基于节点插入，节点消法和重新确定权因子等技术，给出了平面三次NURBS曲线的一种同算法，算法根据给定的光顺准则，自动选择需要光顺的节点，局部修改控制顶点和权因子。     

Real-time knot-tying simulation

The real-time simulation of rope, and knot tying in particular, raises difficult issues in contact detection and management. Some practical knots can only be achieved by complicated crossings of the rope, yielding multiple simultaneous contacts, especially when the rope is pulled tight. This paper describes a graphical simulator that allows a user to grasp and smoothly manipulate a virtual rope and to tie arbitrary knots, including knots around other objects, in real time. A first component of the simulator computes the global configuration of the rope based on user interactions. Another component of the simulator precisely detects self-collisions in the rope as well as collisions with other objects. Finally, a third component manages collisions to prevent penetration, while making the rope slide with some friction along itself and other objects, so that knots can be pulled tight in a realistic manner. An additional module uses recent results from knot theory to identify, also in real time, which topological knots have been tied. This work was motivated by surgical suturing, but simulation in other domains, such as sailing and rock climbing, could also benefit from it.     

Approximation and estimation bounds for free knot splines

The fitting to data by splines has long been known to improve dramatically if the knots can be adjusted adaptively. To demonstrate the quality of the obtained free knot spline, it is essential to characterize its generalization ability. By utilizing the powerful techniques of the empirical process and approximation theory to address the estimation and approximation error bounds, respectively, the generalization ability of the free knot spline learning strategy is successfully characterized. We show that the Pseudo-dimension of free knot splines is essentially a linear function of the number of knots. A class of rather general loss functions is considered here and the squared loss is specially treated for its excellent property. We also provide some numerical results to demonstrate the utility of these theoretical results in guiding the process of choosing the appropriate knot numbers through the training data to avoid the overfitting/underfitting problem.     

曲线拟合的模糊医学图像分割算法

针对由于机体组织间的连续造成医学图像区域边界的模糊性,导致完全自动的医学图像分割算法往往不能取得满意分割效果的问题,提出了一种基于径向基函数曲线拟合的交互式模糊医学图像分割算法。首先由用户在感兴趣的区域交互地选取一些特征点,然后利用径向基函数构造一个三维空间的隐式曲面,使该曲面上的某一等值线即为分割出的区域,文中还提出了两种不同的加速算法。大量实验表明,该算法能有效处理非常模糊的医学图像。     

B样条曲线节点插入和升阶的统一方法

利用 B样条基转换矩阵的有关结果 ,本文给出了 B样条曲线插入节点和升阶的统一方法及算法 .本文方法建立在严密的数学背景上 ,以简洁严谨的递推公式给出其算法的数学模型 ,相应的算法效率高且易实现 ,算法的时间复杂性为 O((k- k 1) kn) ,其中 k,k分别为升阶前后曲线的阶 ,n k 1为插入节点和升阶后的节点数 .而且 ,本文方法及算法使用灵活 ,适用范围广 ,可用于同时插入任意个相同的或不同的节点并升任意阶 ,也可用于只插入节点或只升阶 .Cohen等的 Oslo算法、升阶方法都是本文方法的特例 ,而且本文方法效率更高     

Determining Knots by Minimizing Energy

A new method for determining knots to construct polynomial curves is presented. At each data point, a quadric curve which passes three consecutive points is constructed. The knots for constructing the quadric curve are determined by minimizing the internal strain energy, which can be regarded as a function of the angle. The function of the angle is expanded as a Taylor series with two terms, then the two knot intervals between the three consecutive points are defined by linear expression. Between the two consecutive points, there are two knot intervals, and the combination of the two knot intervals is used to define the final knot interval. A comparison of the new method with several existing methods is included.     

Visualization of Seifert surfaces

The genus of a knot or link can be defined via Seifert surfaces. A Seifert surface of a knot or link is an oriented surface whose boundary coincides with that knot or link. Schematic images of these surfaces are shown in every text book on knot theory, but from these it is hard to understand their shape and structure. In this paper, the visualization of such surfaces is discussed. A method is presented to produce different styles of surface for knots and links, starting from the so-called braid representation. Application of Seifert's algorithm leads to depictions that show the structure of the knot and the surface, while successive relaxation via a physically based model gives shapes that are natural and resemble the familiar representations of knots. Also, we present how to generate closed oriented surfaces in which the knot is embedded, such that the knot subdivides the surface into two parts. These closed surfaces provide a direct visualization of the genus of a knot. All methods have been integrated in a freely available tool, called SeifertView, which can be used for educational and presentation purposes.     

Image approximation by variable knot bicubic splines

This paper presents a degree of freedom or information content analysis of images in the context of digital image processing. As such it represents an attempt to quantify the number of truly independent samples one gathers with imaging devices. The degrees of freedom of a sampled image itself are developed as an approximation problem. Here, bicubic splines with variable knots are employed in an attempt to answer the question as to what extent images are finitely representable in the context of digital sensors and computers. Relatively simple algorithms for good knot placement are given and result in spline approximations that achieve significant parameter reductions at acceptable error levels. The knots themselves are shown to be useful as an indicator of image activity and have potential as an image segmentation device, as well as easy implementation in CCD signal processing and focal plane smart sensor arrays. Both mathematical and experimental results are presented.     

基于极小化二阶导矢确定节点

构造参数拟合曲线的关键问题之一是为每个数据点指定一个参数值（节点）。提出了一种确定节点的新方法。对于每个数据点,新方法由相邻的三个数据点构造一条二次多项式曲线,二次曲线的节点通过极小化其二阶导矢的平方确定。两个相邻数据点间的节点区间由两条二次曲线确定。为使节点计算公式能有效反映出相邻数据点的变化情况,新方法改进了修正弦长方法并应用于节点计算。新方法是一个局部化方法,因此适合于曲线曲面的交互设计。实验结果说明,新方法比其他节点计算方法有效。     

基于广义逆矩阵的B样条曲线节点消去算法研究

为了能运用广义逆矩阵理论来研究B样条曲线的节点消去问题，以解决在B样条曲线曲面拟合过程中产生的冗余节点数据，提出了一种基于广义逆矩阵的B样条曲线节点消去算法，该算法首先利用广义逆矩阵在处理奇异性问题上的独特作用来获得B样条曲线的节点可以消去的充要条件；然后在此基础上，又提出了消去多个节点的算法，算法对每个可以消去的节点都可计算相应的广义逆矩阵，而且仅进行一次矩阵的相乘即可得到由消去这个节点而产生的新的控制顶点和节点。实验表明，该算法的精度优于或近似于现有的Tiller算法，而时间效率则同于或近似于Tiller的算法。由于通过调整算法中的误差阈值，可以有效地控制消去节点后的曲线与原来曲线的误差，因此算法可以用于工程实践。     

Subdivision Surfaces with Creases and Truncated Multiple Knot Lines

We deal with subdivision schemes based on arbitrary degree B‐splines. We focus on extraordinary knots which exhibit various levels of complexity in terms of both valency and multiplicity of knot lines emanating from such knots. The purpose of truncated multiple knot lines is to model creases which fair out. Our construction supports any degree and any knot line multiplicity and provides a modelling framework familiar to users used to B‐splines and NURBS systems.     

B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method

The location of knot points and estimation of the number of knots are undoubtedly known as one of the most difficult problems in B-Spline curve approximation. In the literature, different researchers have been seen to use more than one optimization algorithm in order to solve this problem. In this paper, Big Bang-Big Crunch method (BB-BC) which is one of the evolutionary based optimization algorithms was introduced and then the approximation of B-Spline curve knots was conducted by this method. The technique of reverse engineering was implemented for the curve knot approximation. The detection of knot locations and the number of knots were randomly selected in the curve approximation which was performed by using BB-BC method. The experimental results were carried out by utilizing seven different test functions for the curve approximation. The performance of BB-BC algorithm was examined on these functions and their results were compared with the earlier studies performed by the researchers. In comparison with the other studies, it was observed that though the number of the knot in BB-BC algorithm was high, this algorithm approximated the B-Spline curves at the rate of minor error.     

基于改进YOLOv3的木结缺陷检测方法研究

针对木条表面死结和活结缺陷在检测过程中定位困难、平均识别精确度较低、检测速度较慢的问题,在分析木结缺陷特点和改进深度学习YOLOv3模型的基础上,研究其应用于改善木结缺陷检测时的精确度和速度。首先,对活结缺陷图像进行数据扩增,以解决类别不平衡问题。然后,改进k-means++算法,提升木结缺陷目标框的维度聚类效果,得到更合适的初始目标框个数与尺寸;通过缩减YOLOv3中多尺度检测网络、改进损失函数,以减少检测时间和提高目标识别精确度。最后,对木结缺陷进行拼接得出位置坐标。试验结果表明,较改进前YOLOv3算法,mAP值提升7.47%,检测速度提高35%;较Faster R-CNN算法mAP值提升11.68%,检测速度提高约15倍,改进后模型能精确地检测出死结和活结缺陷。因此,在后续研究中,可考虑以YOLOv3算法作为检测木结缺陷模型,进一步改进YOLOv3网络,以提高检测实时性和精确度。     

B样条曲线的节点插入问题及两个新算法

Ｂｏｅｈｍ算法和Ｏｓｌｏ算法是Ｂ产条曲线的节点插入的经典算法，它们可以有效地将节眯插入到端点插值（Ｅｎｄｏｐｏｉｎｔ－ｉｎｔｅｒｏｌａｔｉｎｇ）Ｂ样条曲线，但是，对于其它的Ｂ样条曲线而言，当插入靠近节眯矢量两端附近的节点时，所有的经典算法都将出错，本文提出了两个节点插入新算法，它们可以解决节插入的经典算法中的问题，能够将任意节点插入到各种Ｂ样条曲线之中，它们的另一个重要用途是可以用于各种Ｂ样条曲线     

Adaptive knot placement using a GMM-based continuous optimization algorithm in B-spline curve approximation

One of the key problems in using B-splines successfully to approximate an object contour is to determine good knots. In this paper, the knots of a parametric B-spline curve were treated as variables, and the initial location of every knot was generated using the Monte Carlo method in its solution domain. The best km knot vectors among the initial candidates were searched according to the fitness. Based on the initial parameters estimated by an improved k-means algorithm, the Gaussian Mixture Model (GMM) for every knot was built according to the best km knot vectors. Then, the new generation of the population was generated according to the Gaussian mixture probabilistic models. An iterative procedure repeating these steps was carried out until a termination criterion was met. The GMM-based continuous optimization algorithm could determine the appropriate location of knots automatically. A set of experiments was then implemented to evaluate the performance of the new algorithm. The results show that the proposed method achieves better approximation accuracy than methods based on artificial immune system, genetic algorithm or squared distance minimization (SDM).     

Wavelet-Based Data Reduction of B-Spline Curves

1 Introduction The problem of reducing the amount of data in the representation of a function or a curve is not new. Many papers have already been published. In these strategies, two trends can be emphasized[1]. The first one deals with polygonal curves for approximating data[2],[3]. Another approach is based on spline curves[4]~[8]. In the first approach, the problem is formulated so that the perpendicular distance of each point on the curve to the fitted line segments is within a predefined…     

改进C-V模型的木材缺陷彩色图像分割研究

分析了木材节子缺陷图像的特点,将彩色图像作为一个整体的图像进行处理,保护了彩色图像信息的特性,提出了一种基于AOS的扩展C-V矢量模型及背景填充耦合的木材节子缺陷彩色图像分割算法。对Chan-Vese提出的基于Mumford-Shah模型的水平集矢量图像分割模型进行了改进,使分割速度得到了提高;用AOS算法改进了原模型的差分格式,使得差分格式无条件稳定;结合背景填充技术,使得到的新图像缩减了目标与背景间的特征差别。实验结果表明该方法可以较好地实现对木材死节、活节和虫眼等缺陷的彩色图像分割,也可实现对单板多节子缺陷彩色图像的分割,为木材缺陷边缘检测提供一种行之有效的方法。