摘要
摄像机标定技术是计算机视觉领域的一个关键技术,其中的自标定算法就是仅通过一系列的图像来计算摄像机的内参数。Kruppa方程法不仅需要计算基础矩阵还要计算随着图像的不同而改变的外极点。Hartley推导出的基于基础矩阵的简单形式,不需要计算外极点。根据这种形式将摄像机的自标定转化为数学上求代价函数最小化的问题。对于多视图像进行加权,其中的加权因子和计算的基础矩阵以及匹配点的准确性有关,进而构成联合的摄像机自标定算法。本文算法不需要计算变化的外极点,并通过遗传算法来实现其最优化问题,简单易行。实验结果表明了该算法的有效性,并可作为一种通用的标定工具,用于摄像机阵列的标定。
Camera calibration is a key technology in computer vision field, in which self-calibration is to compute camera intrinsic parameters only from a series of images. The Kruppa＇s equations method not only needs to compute the fundamental matrix which includes all of the geometricaly relation between images, but also needs to compute the epipoles which variate with the different images. Hartley deduced a simple form of Kruppa＇s equations in terms of fundamental matrix. The goal of this paper is to convert the equations into the form of cost function according to the Hartley＇s deduction and calculate the cost function by the sum of fundamental matrixes multiplying the corresponding weighting factors that are related with the number of matching features. The genetic optimal algorithms are used to obtain the minimum value of the cost function. Our algorithm doesn＇t require computing the images epipoles so that it avoids the instability of the results and reduces the calculation burden. Experimental results show that the proposed algorithm is more effective and accurate and can become a versatile tool for camera calibration, which will be used in our cameras array calibration.
出处
《电子器件》
CAS
2008年第1期290-295,共6页
Chinese Journal of Electron Devices
基金
国家自然科学基金资助项目(60672052、60572127)
上海市曙光计划项目资助(06SG43)
上海市重点学科建设项目资助(T0102)
关键词
自标定
基础矩阵
遗传算法
Self-calibrations Fundamental Matrix
Genetic Algorithms
