摘要
为了实时地稳定摄像系统的输出视频,提出了一种基于特征匹配的实时电子稳像算法。将改进的Kanade Lucas Tomasi(KLT)算法用于特征提取,并提出一种新的基于灰度投影均值的特征匹配算法。为了保证稳像结果的鲁棒性,还给出了特征的有效性检验方法。另外,基于高实时性对运动滤波算法的要求,在有意运动参数估计中采用了递归Kalman滤波法。实验表明,在微机上稳定单帧图像仅需24.7ms,能够满足实时性要求,且具有良好的稳像效果。
<Abstrcat>In order to stabilize the output video of camera system in good time, a real-time electronic image stabilizing algorithm based on feature matching is presented. The improved Kanade-Lucas-Tomasi (KLT) algorithm is applied to feature extracting, and a new feature matching algorithm based on projection mean is put forward. Considering the reliability of stabilizing results, a feature validation test method is also given. Besides, based on the high real-time demand for motion filtering, the recursive Kalman filtering is applied in intentional motion parameter estimation. As experiments show, the algorithm just takes 24.7ms to stabilize a single image on PC, fast enough to satisfy the real-time demand, and it has good stabilizing results.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2005年第3期45-48,109,共5页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(10376043(A06))
关键词
电子稳像
KLT特征提取
特征匹配
特征有效性检验
递归Kalnan滤波
electronic image stabilization
KLT feature extracting
feature matching
feature validation test
recursive Kalman filtering
