摘要
提出了一种利用加权迭代来抛弃出格数据的自定标算法.首先利用加权求欧氏重建及各图像点的重投影误差,再利用各点的重投影误差作为下次迭代的权值,如此循环,使出格数据的权值接近于0,最后完成摄像机的自定标.该算法可以克服最小二乘法鲁棒性差及随机抽样算法计算量大的缺点,具有运算量小、鲁棒性好等优点.
The paper presents a robust self-calibration algorithm based on iterative weight, which can efficiently discard the outhers. The weight of each point is determined based on the re-projective errors and the metric reconstruction is obtained based on the weight. After serveral iterations, the weights of the outliers approaches zero, with the projective reconstruction obtained with good accuracy. The camera intrinsic parameters are obtained after projective reconstruction. The algorithm can overcome the drawbacks of both the least-squares method and the RANSAC method. The theory and experiments with both simulation and real data demonstrate that the self-calibration algorithm is very efficient and robust.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2005年第5期663-666,共4页
Journal of Xidian University
基金
国家自然科学基金资助项目(60473119
60372043)
关键词
自定标
重投影误差
加权迭代
serf-calibration
re-projection error
iterative weight
