基于模糊核聚类的鲁棒性基础矩阵估计算法

提出一种基于模糊核聚类的鲁棒性基础矩阵估计算法。算法提取匹配点的余差作为特征,利用核函数将一维非线性可分特征映射到高维可分空间,在高维特征空间利用模糊均值分类将匹配点分为内点集和外点集;用高斯函数分别对已分类的内点集和外点集进行建模,定义并计算两类高斯分布的可分性判定值;判断该判定值是否收敛,如未收敛则以内点集作为初始值重新迭代计算。模拟数据和真实数据的基础矩阵估计实验表明,本文算法在计算效率和精度上均优于经典的随机抽样一致性算法。

Robust fundamental matrix estimation based on kernel fuzzy clustering

A robust fundamental matrix estimation algorithm based on kernel fuzzy clustering is proposed.The residual features of match points are detected by this algorithm.Using the kernel functions,the nonlinear dividable features in the original one-dimensional space can be mapped to a high-dimensional feature space,in which the clustering can be performed effectively.The match points are classified into inliers and outliers by the fuzzy clustering method.The inliers and the outliers are modeled by Gaussian function,and the judgment values of the divisibility of the two sets are defined and calculated.The iteration will continue until the judgment value converges.Experimental results of the fundamental matrix computation on both simulated and real data demonstrate the superiority of the proposed algorithm in precision and efficiency over classical RANdom SAmple Consensus(RANSAC) algorithm.