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基础矩阵估计的聚类分析算法 被引量：9

Clustering Algorithm for the Fundamental Matrix Estimation
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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. 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.
作者 陈付幸 王润生
机构地区 国防科学技术大学ATR国家重点实验室
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2005年第10期2251-2256,共6页 Journal of Computer-Aided Design & Computer Graphics
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 fundamental matrix Gaussian mixture model robust random sample consensus EM split-merge EM
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献13

  • 1陈泽志,吴成柯.一种高精度估计的基础矩阵的线性算法[J].软件学报,2002,13(4):840-845. 被引量：15
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二级参考文献22

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共引文献67

同被引文献76

引证文献9

二级引证文献48

计算机辅助设计与图形学学报
2005年 第10期

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