基于多核学习-密度峰值聚类的基础矩阵估计

Fundamental Matrix Estimation Based on Multiple Kernel Learning-Density Peak Clustering

现有基础矩阵鲁棒估计方法存在精度不高、准确性较低等不足,基于此,提出一种利用多核学习改进密度峰值聚类的基础矩阵估计方法。首先,针对密度峰值算法需要选取参数和无法自动聚类等不足,引入多核学习和γ分布图进行改进;其次,以对极距离为特征,通过多核学习-密度峰值算法剔除匹配数据集中的异常值,得到较优内点集;最后,使用M估计法消除定位噪声误差,对内点子集进行进一步优化处理,并估计最终的基础矩阵。利用INRIA Dataset数据集对所提方法进行验证分析。结果表明:在保证匹配点信息较多的前提下,所提方法提高了基础矩阵的计算精度和准确性。

Existing robust estimation methods of the fundamental matrix possess some limitations such as low accuracy.This study presents a fundamental matrix estimation method that uses multi-kernel learning to improve density peak clustering.First,from the viewpoint of the shortcomings in the density peak algorithm,such as the need to select parameters and inability to automatically cluster,multi-kernel learning and γ distribution map are introduced.Second,with the feature of epipolar distance,the proposed method eliminates the anomaly of the matching dataset to obtain a better internal point set.Finally,the M estimation method is used to exclude the positioning noise error,conduct further optimization processing on the internal idea set,and estimate the final base matrix.The INRIA dataset is used to validate and analyze the proposed method.Results show that the calculation accuracy and correctness of the fundamental matrix are improved using the proposed method.