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Estimation of Translation, Rotation and Scaling Based on Multiple Motion Estimations

机译:基于多重运动估计的平移,旋转和缩放比例估计

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We will be able to use highly parallel processing environments. This paper proposes a method for estimating translations, rotations and scaling simultaneously from the estimated motion vectors, and represents its performance with motion estimation experiments. A sector region luminosity correlation is used for estimating motion vectors. The sector region luminosity correlation is robust about the rotation and withstands large motion environments. The proposed method makes the assumption about the scaling and estimates the motion vectors based on the assumptions. Then it randomly creates the pair of the estimated motion vectors. Next, it selects the proper pair using the scaling factor of the pair. The selected pairs are included in the set of reliable motion vector pairs. The reliable motion vector pairs decide the translation, rotation and scaling. With large scaling, it is difficult to estimate the motion using the sector region luminosity correlation. But with the assumptions about the scaling, they can work. Experiments show that the proposed method makes much better correlations between images than SIFT does.
机译:我们将能够使用高度并行的处理环境。本文提出了一种从估计的运动矢量同时估计平移,旋转和缩放的方法,并通过运动估计实验来表示其性能。扇区区域的亮度相关性被用于估计运动矢量。扇形区域的亮度相关性在旋转方面很强,并且可以承受较大的运动环境。所提出的方法做出关于缩放的假设并且基于该假设估计运动矢量。然后,它随机创建一对估计的运动矢量。接下来,它使用线对的比例因子选择合适的线对。所选对包括在可靠运动矢量对集合中。可靠的运动矢量对决定平移,旋转和缩放。对于大比例缩放,很难使用扇区区域的亮度相关性来估计运动。但是有了有关扩展的假设,它们就可以工作。实验表明,与SIFT相比,该方法在图像之间具有更好的相关性。

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