计算机工程与应用2009，Vol.45Issue(21)：55-57,60,4.DOI:10.3778/j.issn.1002-8331.2009.21.014
基于自适应神经网络的双摄像机标定
Self-adaptive neural network for binocular camera calibration
摘要
Abstract
Camera calibration is an important step in computer vision,the traditional calibration methods need to acquire the in-trinsic and extrinsic parameters and the process is quite complicated,so self-adaptive neural network is used to learn the rela-tionship between the image coordinates and the space coordinates.The generalization ability is improved a lot by the following methods:modify Harris comer extraction algorithm to improve the training data accuracy,choose the number of the middle layer cells adaptively through program,and combine normalization and stopped training strategies.At last,comparing with the traditional calibration method,the test result shows that this method is available and has higher precision for binocular camera calibration.关键词
双摄像机标定/自适应/多层前馈网络/Harris角点Key words
binocular camera calibration/self-adaptive/Back Propagation(BP) network/harris comer分类
计算机与自动化引用本文复制引用
崔岸,袁智,王龙山..基于自适应神经网络的双摄像机标定[J].计算机工程与应用,2009,45(21):55-57,60,4.基金项目
国家重点实验室基金资助项目(No.KLVBDM2005003) （No.KLVBDM2005003）
吉林省科技发展计划基金资助项目(No.20080539). （No.20080539）
