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基础矩阵估计的聚类分析算法
引用本文:陈付幸,王润生.基础矩阵估计的聚类分析算法[J].计算机辅助设计与图形学学报,2005,17(10):2251-2256.
作者姓名:陈付幸  王润生
作者单位:国防科学技术大学ATR国家重点实验室,长沙,410073
摘    要:提出一种基于聚类分析的Robust基础矩阵估计算法.该算法用高斯混合模型描述匹配点估计余差,采用改进的分裂合并EM算法对匹配点估计余差进行聚类分析,根据分类结果及平均余差最小规则筛选出正确匹配点类别,抛弃错误匹配点;最后,用M估计算法对筛选出的正确匹配点进行迭代求精.大量实验结果表明,文中算法比随机抽样一致性算法的估计精度高,且计算效率高.

关 键 词:基础矩阵  高斯混合模型  鲁棒性  随机抽样一致性算法  EM算法  分裂合并EM算法
收稿时间:2004-06-10
修稿时间:2004-06-102004-10-10

Clustering Algorithm for the Fundamental Matrix Estimation
Chen Fuxing,Wang Runsheng.Clustering Algorithm for the Fundamental Matrix Estimation[J].Journal of Computer-Aided Design & Computer Graphics,2005,17(10):2251-2256.
Authors:Chen Fuxing  Wang Runsheng
Affiliation:National Key Laboratory of ATR , National University of Defense Technology, Changsha 410073
Abstract:In the paper, Gaussian mixture model is used to describe the residuals of matches in the new robust algorithm for fundamental matrix estimation, and an improved split-merge EM (SMEM) algorithm is used to classify the matches, so that the false matches can be detected and rejected by the least mean absolute residual criteria. Finally, M-estimator is used to estimate the fundamental matrix. Our algorithm gives better result than random sample consensus (RANSAC) algorithm with higher efficiency in the large number experiments tested.
Keywords:fundamental matrix  Gaussian mixture model  robust  random sample consensus  EM  split-merge EM
本文献已被 CNKI 维普 万方数据 等数据库收录!
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