基于遗传算法不同策略下的基础矩阵估计方法

在未定标系统中，对极几何约束给出了图像间的全部信息，成为解决许多视觉问题的关键环节，提出了一种基于遗传算法不同策略下的基础矩阵估计方法，它利用每个基因代表一个匹配点，每条染色体作为基础矩阵计算时的最小子集，并根据染色体长度决定采用何种策略估计基础矩阵，此方法在很大程度上减小了出格点对估计过程的影响，能够较好地汇聚到全局最优解，模拟数据和真实图像的实验结果都表明，所给出的方法能够有效地检测和删除错定位和误匹配点，提高了基础矩阵估计的鲁棒性和精度。

A METHOD OF FUNDAMENTAL MATRIX ESTIMATION BASED ON GENETIC ALGORITHM USING DIFFERENT STRATEGIES

Two perspective images of single scene taken by uncalibrated perspective cameras are constrained by the epipolar geometry, which is the key to many problems of computer vision. The problem of robust fundamental matrix estimation employing a new method is addressed based on genetic algorithm using different strategies. The method uses each gene to stand for a pair of correspondences, takes every chromosome as a minimum subset for epipolar geometry estimation, and computes the fundamental matrix according to the length of the chromosomes. The method would eventually converge to a globally optimal solution and is relatively unaffected by the outliers. Experiments with both synthetic data and real images show that the method is more robust and precise than other typical methods because it can efficiently detect and delete the bad corresponding points, which include both bad locations and false matches.