摘要
摄像机标定是精密视觉测量的基础,传统的双目标定位需要建立复杂的数学模型。神经网络可以有效地处理非线性映射问题,本文介绍了一种BP神经网络,可以很好地描述双目视觉中三维空间特征点坐标和2个摄像机对应像点间的非线性关系,并且为了提高网络的学习能力引入了动态因子。将神经网络标定方法与传统的常用标定方法比较,实验结果表明,基于神经网络的双目视觉标定方法能获得较高的标定精度。
An accurate camera calibration method is required to achieve precise visual measurements,and traditional binocular calibration methods involve complicated mathematical models. As neural networks are effective for dealing with non-linear mapping, in the paper a BP neural network is proposed and implemented,which could satisfactorily describes the non-linear relations between 3-D characteristic points and their corresponding stereo images in the binocular vision. In order to improve the learning ability of the network,a dynamic gene is also introduced to the BP algorithm. Compared with traditional calibration methods, experimental results show that the proposed binocular calibration method based on neural network could obtain high accuracy.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2005年第9期1097-1100,共4页
Journal of Optoelectronics·Laser
关键词
神经网络
双目视觉
摄像机标定
BP算法
neural network； binocular vision； camera calibration
BP algorithm
