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A real-time gray projection algorithm for electronic image stabilization

机译:用于电子图像稳定的实时灰度投影算法

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Electronic digital image stabilization technique plays important roles in video surveillance or object acquisition. Researchers have presented many useful algorithms, which can be classified to three kinds: gray based methods, transformation based methods and feature based methods. When scenario is simple or flat, feature based methods sometimes have imperfect results. Transformation based methods usually accompany large computation cost and high computation complexity. Here we presented an algorithm based on gray projection which divided the whole image into four sub-regions: the upper one, the bottom one, the left one and the right one. For making the translation estimation easier, a central region is also chosen. Then the gray projections of the five sub-regions were counted. From the five pairs of gray projections five group offsets including rotation and translation were obtained via cross correlation between current frame and reference frame gray projections. Then according to the above offsets, the required parameters can be estimated. The expected translation parameters(x axis offset and y axis offset) can be estimated via the offsets from the central region image pair, the rotation angle can be calculated from the left four groups offsets. Finally, Kalman filter was adopted to compute the compensation. Test results show that the algorithm has good estimation performance with less than one pixel translation error and 10 percent rotation error. Based on this kind of gray projection algorithm, a real-time electronic digital image stabilization system has been designed and implemented. System tests demonstrate the system performance reaches the expected aim.
机译:电子数字图像稳定技术在视频监视或对象获取中起着重要作用。研究人员提出了许多有用的算法,这些算法可以分为三种:基于灰色的方法,基于变换的方法和基于特征的方法。当场景简单或平坦时,基于特征的方法有时会产生不完美的结果。基于转换的方法通常伴随着较大的计算成本和较高的计算复杂性。在这里,我们提出了一种基于灰度投影的算法,该算法将整个图像分为四个子区域:上部,下部,左侧和右侧。为了使翻译估计更容易,还选择了中心区域。然后计算了五个子区域的灰色投影。通过当前帧和参考帧灰度投影之间的互相关,从五对灰度投影中获得包括旋转和平移在内的五组偏移。然后根据上述偏移量,可以估算出所需的参数。预期的平移参数(x轴偏移和y轴偏移)可以通过从中心区域图像对偏移来估计,旋转角度可以从左四组偏移来计算。最后,采用卡尔曼滤波器进行补偿。测试结果表明,该算法具有良好的估计性能,像素平移误差小于1,旋转误差小于10%。基于这种灰度投影算法,设计并实现了一种实时电子数字图像稳定系统。系统测试表明系统性能达到了预期目标。

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