A simple algorithm for detection of significant vertices for polygonal approximation of chain-coded curves

A simple but efficient method is proposed for detection of significant points of chain-coded curves. The polygonal approximation is achieved by joining successive significant vertices. The algorithm is based on manipulation with chain codes only.     

基于边界曲线弧分割的多椭圆检测

不同于传统的Hough变换算法,提出一种基于边界弧分割的椭圆检测方法。首先将边界从交点处分割成弧段,将得到弧段划分为长弧和短弧两组并按长度降序排序,然后从两组中找出属于某个椭圆的若干弧段,利用最小二乘法拟合得到候选椭圆并验证是否为真正椭圆。实验表明该算法能快速检测出图中椭圆,运行时间远小于采用随机Hough变换算法,在具有噪声、椭圆残缺的情况下仍能有较好的检测结果。     

一种基于离散微粒群优化的数字曲线的多边形近似算法

数字曲线的多边形近似是图像分析研究领域的一个热点问题.获取数字曲线的优化多边形近似是一个复杂的问题,其计算复杂度非常高.微粒群算法是近些年来提出的一种新的优化方法,已经被广泛应用于各种优化问题的求解.提出了一种求解数字曲线的多边形近似问题的基于整数编码的离散微粒群算法(IPSO).IPSO通过重新定义标准微粒群算法的速度和位置更新公式中的加法、乘法和减法运算,使得算法能运行在离散的解空间.IPSO的位置向量修复机制保证了解的可行性,而局部优化器提高了算法的搜索精度.实验结果表明,IPSO求解的质量和求解的效率均优于遗传算法和0-1编码的微粒群算法.     

一种改进的多边形近似算法

多边形近似是曲线矢量数据压缩技术中的一种,其实质是多边形信息压缩问题,目的在于减少多边形曲线数据的冗余信息,释放所占用的空间,达到高效、快速地显示图形。通过分析基于显著点删除的多边形近似算法的特性,发现基于显著点删除算法由于没有考虑多边形与多边形之间的联系以及多边形中特殊点的问题,以至于在处理具有孤立点等情况时的多边形近似效果不理想。为了更好地解决上述缺陷,提出了一种改进的多边形近似算法。通过分析现有算法,发现在处理特殊孤立点、边界处裂缝、多点处于同一直线时处理效果不理想,针对这些问题,通过孤立点单独处理、边界点建立索引以及根据多边形形状忽略处于同一直线上的多点方法,对算法进行改进。同时利用数据集对改进算法性能进行分析,实验结果表明,改进的算法在多边形近似处理效率和效果上更加明显。     

基于Franklin函数的数字曲线多边形逼近

针对数字曲线多边形逼近中存在计算复杂度高、容易受噪声影响等问题,提出一种基于Franklin函数系的数字曲线多边形逼近算法.通过对原始数字曲线在Franklin函数系下进行正交分解,选取少量较大的、反映了原始数字曲线主要特征的分解系数进行重构,所得结果即为数字曲线的逼近多边形.实例结果表明,该算法计算复杂度低、对噪声有较强的鲁棒性,不仅可以实现对原始数字曲线的多层次的最佳平方逼近,而且还保留了原始数字曲线的整体特征.     

一种改进的骨架曲线串行多边形近似算法

常见骨架提取算法对复杂多变的目标边缘具有较强的敏感性, 提取出的骨架曲线结构相对复杂, 数据量仍然较大. 针对这一问题, 提出了一种新的骨架曲线多边形近似算法. 该算法结合骨架曲线的特点, 在传统串行多边形近似算法的基础上引入了平滑度保持、结构特征保持以及拓扑特征保持等约束条件, 既较好地保留了原始骨架的主要拓扑结构特征, 又有效地简化了骨架曲线的结构, 进一步压缩了数据. 仿真研究证明了该方法的有效性.     

A new measurement for assessing polygonal approximation of curves

This paper presents a novel method for assessing the accuracy of unsupervised polygonal approximation algorithms. This measurement relies on a polygonal approximation called the “reference approximation”. The reference approximation is obtained using the method of Perez and Vidal [11] by an iterative method that optimizes an objective function. Then, the proposed measurement is calculated by comparing the reference approximation with the approximation to be evaluated, taking into account the similarity between the polygonal approximation and the original contour, and penalizing polygonal approximations with an excessive number of points. A comparative experiment by using polygonal approximations obtained with commonly used algorithms showed that the proposed measurement is more efficient than other proposed measurements at comparing polygonal approximations with different number of points.     

Relaxation algorithm for shape matching of two-dimensional objects

This paper describes a relaxation algorithm without a parameter for shape matching of partially occluded two-dimensional objects. This technique is based on a relaxation labelling process of Rosenfeld et al. for reducing or eliminating the ambiguity. The shapes are represented by polygonal approximation. The proposed relaxation method is used to find acceptable line segment pairs of polygons.     

遗传算法用于曲线的误差约束多边形近似

提出了一种求解曲线的误差约束多边形近似问题的遗传算法.其主要思想是:1)采用变长染色体编码机制,以减少存储空间和计算时间的消耗;2)针对问题的特点,提出了一种新的杂交算子——基因消去杂交,以尽可能地消去染色体上的冗余基因,从而提高算法的寻优能力;3)采用染色体修复策略处理遗传操作产生的不可行解,该策略通过迭代地向染色体追加有价值的候选基因来实现染色体的修复,并提出一种对染色体的候选基因进行评估的机制.通过实验评估并与其他遗传算法进行比较,结果表明,提出的算法性能更优越.     

一种轮廓曲线的多边形近似算法

首先基于相邻像素间的欧氏距离计算出轮廓曲线上每一点的支持区域及支持半径,用线性插值得到支持区域的端点;然后求得支持区域的质心,以曲线上的点和其相应的支持区域质心之间的距离作为特征响应筛选出候选特征点;最后将具有局部极大特征强度的点作为多边形的顶点。实验表明这种方法能降低曲线数字化所带来的影响,对旋转也具有较高的不变性。     

Genetic particle swarm optimization for polygonal approximation of digital curves

Polygonal approximation is an important technique in image representation which directly impacts on the accuracy and efficacy of the subsequent image analysis tasks. This paper presents a new polygonal approximation approach based on particle swarm optimization (PSO). The original PSO is customized to continuous function value optimization. To facilitate the applicability of PSO to combinatorial optimization involving the problem in question, genetic reproduction mechanisms, namely crossover and mutation, are incorporated into PSO. We also propose a hybrid strategy embedding a segment-adjusting-and-merging optimizer into the genetic PSO evolutionary iterations to enhance its performance. The experimental results show that the proposed genetic PSO significantly improves the search efficacy of PSO for the polygonal approximation problem, and the hybrid strategy can accelerate the convergence speed but still with good-quality results. The performance of the proposed method is compared to existing approaches on both synthesized and real image curves. It is shown that the proposed hybrid genetic PSO outperforms the polygonal approximation approaches based on genetic algorithms and ant colony algorithms. The text was submitted by the author in English. Peng-Yeng Yin was born in 1966 and received his B.S., M.S. and Ph.D. degrees in Computer Science from National Chiao Tung University, Hsinchu, Taiwan, in 1989, 1991 and 1994, respectively. From 1993 to 1994, he was a visiting scholar at the Department of Electrical Engineering, University of Maryland, and the Department of Radiology, Georgetown University. In 2000, he was a visiting Associate Professor in the Visualization and Intelligent Systems Lab (VISLab) at the Department of Electrical Engineering, University of California, Riverside (UCR). He is currently a Professor at the Department of Information Management, National Chi Nan University, Nantou, Taiwan. His current research interests include image processing, pattern recognition, machine learning, computational biology, and evolutionary computation. He has published more than 70 articles in refereed journals and conferences. Dr. Yin received the Overseas Research Fellowship from the Ministry of Education in 1993 and Overseas Research Fellowship from the National Science Council in 2000. He is a member of the Phi Tau Phi Scholastic Honor Society and listed in Who’s Who in the World.     

多边形近似曲线的基于排序选择的拆分合并算法

将遗传算法的排序选择策略引入到传统的拆分与合并算法,提出一种基于排序选择策略的拆分与合并算法（RSM）来求解平面数字曲线的多边形近似,解决了传统的拆分与合并算法对初始解的依赖问题．用2条通用的benchmark曲线对RSM算法进行测试,结果表明该算法的性能优于遗传算法和传统的拆分与合并算法．将RSM算法应用于湖泊卫星图像的多边形近似,取得了较好的近似效果．     

一种基于遗传算法的数字曲线多边形逼近方法

提出了一种基于遗传算法的数字曲线多边形改进逼近方法。该方法针对规则形状数字曲线的多边形逼近问题,以二进制向量序列表示的染色体作为每一个对应的逼近多边形候选解,将简化前后多边形质心偏移误差以及各被替换线段欧氏距离的方差引入到适应函数中,用迭代次数的sigmoid函数作为变异概率来控制遗传算法优化求解过程中的全局和局部搜索特性。实验结果表明,该方法对于保持曲线多边形简化逼近后的形状特征具有较好的效果。     

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．     

基于矩的数字图像多边形逼近方法

提出了两种基于矩的数字图像的多边形逼近方法。通过比较原始图形和近似图形之间几体矩或Legendre矩的偏差的大小,选择一个最佳的近似结果,进一步可以得到一个顶点数递减的近似多边形序列。与现存的方法比较,这种方法有效地避免了逼近这结果依赖于起始点的选取的缺陷。     

An algorithm for detection of dominant points and polygonal approximation of digitized curves

A new technique for the detection of dominant points and polygonal approximation of digitized curves is proposed. The procedure needs no input parameter and remains reliable even when features of multiple size are present. Unlike the existing algorithms, the present technique introduces the concept of an asymmetric region of support and k-l-cosine. The dominant points are the local maxima of k-l-cosine. The polygon is obtained joining the dominant points successively.     

基于遗传算法的多边形逼近3D数字曲线

首先对3D数字曲线进行简单的数据压缩．通过对该曲线上的点列进行二进制编码定义来表示数字曲线的染色体．二进制串中的每一个位称为基因,每一个逼近多边形和染色体形成1-1映射．目标函数使给定曲线和逼近多边形之间的均方差最小．构造了解决该问题的选择、交叉、变异三个算子．所得最优染色体中基因值为1的基因对应数字曲线的分界点．实验结果表明,该方法能够得到精确的逼近结果．     

基于SOFM的遥感图像的机场区域变化检测

文章提出了一种新的针对遥感图像机场感兴趣区域变化检测的方法,该方法采用自组织特征映射神经网络对图像进行分类,结合感兴趣区域位置定位结果,利用区域生长分割出感兴趣区域。通过图像间差值,从而获得差异,并利用形状分析的方法对检测出的变化给出了解释。该方法消除了非感兴趣区域变化造成的虚警,克服了多幅图像由于摄取时间和摄取环境的差异造成的影响,提高了检测率。实验表明该文提出的方法具有良好的效果。