A New Fundamental Matrix Estimation Algorithm of High Accuracy and Robustness

ZHANG Yong-xiang; MU Tie-ying; ZHANG Wei-gong; GU Pei-qiang

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A New Fundamental Matrix Estimation Algorithm of High Accuracy and Robustness

更新时间：2026-09-04

- A New Fundamental Matrix Estimation Algorithm of High Accuracy and Robustness
- Vol. 33, Issue 3, Pages: 32-36(2016)
- 作者机构：
  
  1. 首都师范大学机构信息工程学院,北京,102248
  2. 首都师范大学高可靠嵌入式系统北京市工程技术研究中心,北京,100048
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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张永祥, 穆铁英, 张伟功, 古佩强. 一种新的估计基础矩阵的高精度鲁棒算法[J]. 微电子学与计算机, 2016, 33(3): 32-36.

ZHANG Yong-xiang, MU Tie-ying, ZHANG Wei-gong, et al. A New Fundamental Matrix Estimation Algorithm of High Accuracy and Robustness[J]. 2016, 33(3): 32-36.
张永祥, 穆铁英, 张伟功, 古佩强. 一种新的估计基础矩阵的高精度鲁棒算法[J]. 微电子学与计算机, 2016, 33(3): 32-36. DOI：

ZHANG Yong-xiang, MU Tie-ying, ZHANG Wei-gong, et al. A New Fundamental Matrix Estimation Algorithm of High Accuracy and Robustness[J]. 2016, 33(3): 32-36. DOI：

摘要

针对基础矩阵估计过程中因异常数据的影响导致精度和稳定性不好等问题

提出一种新的基础矩阵的估计算法.该算法首先对匹配点采用了新的分组抽样策略

突出了随机性和均匀性.然后以对极距离作为准则

通过二次中值的方法获得新的内点集和基础矩阵初值.最后引入动态惩罚加权的思想

对新的匹配点集进行非线性优化

恢复了精确的对极几何关系.大量的模拟数据和真实图像实验数据结果表明

在误匹配和噪声存在的情况下

提出的算法切实可行

能够提高基础矩阵估计的精度和鲁棒性.

Abstract

In view of the dissatisfactory accuracy and stability caused by outliers in the process of estimating fundamental matrix

a novel fundamental matrix estimation algorithm was presented in this paper. First a new packet sampling strategy was adopted to the matched points

which emphasized the randomness and uniformity. Under rules of the distance between the point matches and the corresponding epipolar lines

a new inlier set of matched points and original fundamental matrix were obtained via the twice-mid-value method. Finally

the thought of dynamic punishment weighing was introduced to nonlinearly optimize the new point set

which gained the accurate epipolar geometry. Results of a mass of experiments on simulated data and real images indicate that the proposed approach is a practical way and yields the precise and robust fundamental matrix in the case of mismatch and Gaussian noise.

关键词

Keywords

references

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Zhong H X, Pang Y J, Feng Y P. A new approach to estimating fundamental matrix[J]. Image and Vision Computing, 2006, 24(1): 56-60. DOI: 56

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Menglong Yang, Yiguang Liu, Zhisheng You. Estimating the fundamental matrix based on least absolute deviation[J]. Neurocomputing, 2011, 74(17), 3638-3645. DOI: 3638

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孙亦南, 刘伟军, 马永壮, 等. 一种加权计算基础矩阵的高精度算法[J]. 计算机工程, 2005, 31(15): 186-188. DOI: 186

范昭君, 辛菁. 基于改进最小平方中值算法的基本矩阵估计算法[J]. 新技术新工艺, 2014, 35(7): 51-55. DOI: 51

鲁珊, 雷英杰, 孔韦韦, 等. 基于概率抽样一致性的基础矩阵估计算法[J]. 控制与决策, 2012, 27(3): 425-430. DOI: 425

周凡, 邵世雄, 吴建华, 等. 一种联合直线特征的基础矩阵计算方法[J]. 光学学报, 2013, 33(10): 1-8. DOI: 1

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