【作者】 王兆军；

【导师】 田裕鹏；

【作者基本信息】 南京航空航天大学 ， 测试计量技术及仪器， 2010， 硕士

【摘要】 电子稳像是新一代的稳像技术,它是利用计算机图像处理的方法实现图像序列稳定。因为具有稳像精度高、体积小、重量轻、功耗低以及成本低等优点,它在军事和民用领域得到了广泛的应用。电子稳像的关键技术是全局运动估计和运动补偿。如何提高全局运动估计的速度和精度,如何判断并分离摄像机的场景拍摄和随机抖动,使得运动补偿能够实时准确的稳定图像序列,是目前研究的热点。视频序列的抖动包含平移、旋转和变焦等复杂运动,针对传统角点提取易过于集中在前景运动物体上,匹配步骤复杂导致速度过慢,且将所有角点直接代入计算全局运动参数,从而导致局部外点和误匹配点影响计算精度的问题,本文提出了基于图像分区角点匹配的电子稳像算法。首先利用Harris角点检测算子分区均匀提取参考帧中的角点,利用模板匹配的思想,以每一角点为中心,建立特征窗口。同时为了保证估计精度和速度,通过匹配验证技术,很好地解决了角点特征匹配过程中出现的误匹配问题,减少无效角点对的不利影响。再通过列文伯格-马夸尔特迭代法,计算得到全局运动参数。最后采用基于卡尔曼滤波的图像补偿方法,确定抖动参数并结合图像拼接技术来实现图像补偿,提高输出视频的稳定性和完整性。实验结果表明,该算法对角点的预处理和迭代步骤,保证了运动检测的全局性,具有较高的精度,且补偿结果能够实时跟随摄像系统的扫描运动,输出稳定完整的视频序列。更多还原

【Abstract】 Electronic image stabilization is a new technology to stabilize image sequences with computer image processing. It has a lot of advantages, such as high image stabilizing precision, compact size, lightweight, low power consumption and reasonable price. It has been widely used in military and civil fields. The key technologies of electronic image stabilization are global motion estimation and motion compensation. The research on improving efficiency and accuracy of global motion estimation and distinguishing camera scan from dithering to realize real-time and accurate compensation becomes a hot topic at present.Dithering of the video sequence includes translation, rotation and zoom of complex movements. Traditional corner extraction method is easy to generate a lot of corners focused on moving objects in the scene. And the speed is low because of complex matching steps. If we use all corners to calculate the global motion parameters directly, it will result in accuracy problem because of the existence of local outliers and error matched points. So this paper presents an electronic image stabilization algorithm based on image partition and corner matching. First of all, the corners are extracted evenly from the reference frame by using Harris corner detection operator and then each feature window is built to find the corresponding corners in the current frame. At the same time in order to ensure accuracy and speed of estimation, by matching validation technique, we can solve the error matched problem arising in the course of matching and reduce the adverse effects of invalid corner pairs. Next, the global motion parameters can be obtained through Levenberg-Marquardt iterative method. Finally, the Kalman filter is applied to smooth the motion vectors to obtain the dithering vector and the image mosaic is used to compensate each current frame in order to improve the stability and integrity of the output video.The experimental results show that the pre-processing and iterative steps to the corners of the algorithm ensure the overall of the motion detection with high precision. The compensation results can follow scanning movement of the camera system in real time. At last the stable and integral output video sequence can be obtained.更多还原