| 基于全概率更新的改进RANSAC算法 |
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| 引用本文： | 王可,贾松敏,李秀智.基于全概率更新的改进RANSAC算法[J].控制与决策,2017,32(3):427-434. |
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| 作者姓名： | 王可 贾松敏 李秀智 |
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| 作者单位： | 1. 北京工业大学电子信息与控制工程学院,北京100124;\vspace{-3pt};2. 北京工业大学计算智能与智能系统北京市重点实验室,北京100124,1. 北京工业大学电子信息与控制工程学院,北京100124;\vspace{-3pt};2. 北京工业大学计算智能与智能系统北京市重点实验室,北京100124,1. 北京工业大学电子信息与控制工程学院,北京100124;\vspace{-3pt};2. 北京工业大学计算智能与智能系统北京市重点实验室,北京100124 |
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| 基金项目： | 国家自然科学基金项目(61175087,61105033)；北京工业大学智能机器人“大科研”推进计划项目. |
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| 摘 要： | 针对鲁棒性模型估计问题,提出一种基于全概率更新的改进RANSAC算法.该方法利用混合分布模型获取测试样本点的初始概率估计.在RANSAC算法框架下,根据模型估计与测试样本点对一致集的适应度建立全概率评价准则.在此基础上,采用逆变映射作为采样策略,提高了算法的收敛速度;同时,运用测试点平均概率对所提出算法进行了收敛性分析.最后,通过仿真与实际图像匹配实验进一步验证了所提出算法的有效性与可行性.
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| 关 键 词： | RANSAC 全概率公式 混合概率分布 基础矩阵 |
lmproved RANSAC algorithm based on total probability updating |
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| WANG Ke,JIA Song-min and LI Xiu-zhi.lmproved RANSAC algorithm based on total probability updating[J].Control and Decision,2017,32(3):427-434. |
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| Authors: | WANG Ke JIA Song-min and LI Xiu-zhi |
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| Affiliation: | 1. College of Electronic Information and Control Engineering,Beijing University of Technology, Beijing 100124,China;2. Beijing Key Laboratory of Compulational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124,China,1. College of Electronic Information and Control Engineering,Beijing University of Technology, Beijing 100124,China;2. Beijing Key Laboratory of Compulational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124,China and 1. College of Electronic Information and Control Engineering,Beijing University of Technology, Beijing 100124,China;2. Beijing Key Laboratory of Compulational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124,China |
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| Abstract: | To deal with the robust model estimation, an improved RANSAC algorithm is proposed based on a total probability updating procedure. In this proposed method, the initial probabilities of the test points are estimated by using a hybrid probability model. Under the framework of the RANSAC algorithm, the evaluations that model estimation and test points fit the consistent set are employed to update the probabilities of test points with the total probability theorem. According to this updated probabilities, an inverse mapping-based sampling method is adopted to improve the convergence rate of the proposed algorithm. Moreover, an analysis procedure is established by using the average probability of the test points, which verifies the convergence of the proposed algorithm. Finally, the simulation and real image matching experiments demonstrate the effectiveness and feasibility of the proposed algorithm. |
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