一种基于粒子群优化算法对基于点表示的模型进行特征检测的新方法  

A New Particle Swarm Optimizer Based Method for Detecting Features of Point-based Models

作　　者：姜艳[1] 李谊[1] 权勇[1] 李文辉[1] 张继军[2] 王朝辉[2] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]石家庄机械化步兵学院,石家庄050083

出　　处：《吉林大学学报（理学版）》2008年第2期279-285,共7页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:60573182);教育部博士点基金(批准号:20060183042);吉林省科技发展计划项目基金(批准号:2006052720040531)

摘　　要：基于粒子群算法,提出一种针对基于点表示模型的新特征检测方法,解决了大规模数据模型特征的快速显示问题.该方法对粒子群优化算法进行优化,将其应用于物体空间的特征检测上,实现了多目标搜索.通过对粒子群算法中的粒子、适应度函数、初始结束条件、局部最优解、全局最优解和迭代公式的重新定义,将局部搜索与全局搜索相结合,可快速搜索到多个目标.该算法通过构造可估计局部曲面变化的适应度函数检测特征点,并对特征点做标记,以快速显示出模型的特征.实验结果表明,所提出的特征检测算法适用于对基于点表示的模型的快速特征检测,尤其适用于大规模数据模型.A new particle swarm optimizer based method for detecting features of point-based models is presented in this paper. It solves the fast displaying of the characteristics of large scale models. It applies modified particle swarm optimizer （PSO） to the feature detecting of object space so as to complete the search of multiobjective regions. By redefining the particle, fitness function, initial terminal condition, gBest, pBest and update rule of PSO, the method presented in this paper combines the local search with global search for the purpose of finding multiple targets fast. It detects feature points by fitness function which is able to estimate local surface variation and marks feature points to display the characteristics of model fast. The experiments show that the improved algorithm is suitable for fast detecting features of point-based large scale models.

关 键 词：粒子群优化算法 多目标 特征 曲率 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]