基于混合遗传算法的对极几何估计

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.本文提出了一种基于混合遗传算法的对极几何估计方法,它利用每个基因代表一个匹配点,每条染色体作为对极几何估计最小子集.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(或近似全局)最优解.模拟数据和真实图像的实验结果都表明,本文所给出的方法能够有效地检测和删除错定位和误匹配点,提高了对极几何估计的鲁棒性和精度.

A Method of Epipolar Geometry Estimation Based on Messy Genetic Algorithm

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.This paper addresses the problem of robust estimating the epipolar geometry employing a new method based on messy genetic algorithm,which use each gene to stand for a pair of correspondences,and take every chromosome as a minimum subset for epipolar geometry estimation.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 our 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.