机构地区: 复旦大学信息科学与工程学院计算机科学系
出 处: 《计算机辅助设计与图形学学报》 2006年第8期1149-1154,共6页
摘 要: 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. A novel split-and-merge method with ranking selection (RSM) is proposed for the polygonal approximation of curves. We apply the ranking selection scheme of genetic algorithm to the split-and-merge process and substantially reduce the sensitivity of the traditional split-and-merge method to the initialization solution. The experimental results show that RSM is robust and outperforms the traditional split-and-merge method and the genetic-algorithm. We also apply RSM to the polygonal approximation of the satellite image of lake and obtain satisfactory results.
领 域: [自动化与计算机技术] [自动化与计算机技术]