摘要
在逆向工程中,对基于散乱数据点的曲线重建研究有着重要的意义。曲线可用线段基元逼近。提出使用成长型神经网络以线段为基元的曲线重建新算法。给定某一曲线的散乱点集和一初始折线,新算法优化折线上的顶点位置,使折线更好地逼近散乱点;持续分裂折线上活动性强的顶点和删除活动性最弱的顶点,使折线上顶点的分布更符合散乱点数据的概率分布。实验结果表明,新算法能够取得良好的曲线重建效果。
The study of curve reconstruction based on unorganized data points has great importance in reverse engineering.Curve can be reconstructed with line segment Approximation.The paper presents a new algorithm based on growing cell structures to realize curve reconstruction using line segment.Given a set of unorganized data points and an initial polygonal line,the vertex position of polygonal line can be optimized by using the algorithm to make the vertexes of polygonal line gradually approach the given unorganized data points.In order to make the vertexes of polygonal line distribution coincide the space distribution of unorganized data points,the vertexes which are very active are split and which are least active are deleted continually.Experiment results are given which show that the new algorithm is quite effective.
出处
《工程图学学报》
CSCD
北大核心
2010年第6期51-55,共5页
Journal of Engineering Graphics
基金
国家自然科学基金资助项目(60575023)
安徽高校省级自然科研重点资助项目(KJ2009A019ZKJ2007B311ZC)
关键词
曲线重建
成长型神经网络
散乱点
curve reconstruction
growing cell structures
unorganized data points
