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Global Motion Estimation in Sprite Generation by Eliminating Local Object Motions

机译:通过消除局部对象动作来产生全局运动估计

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In this paper, a new feature point selection method for the global motion estimation (GME) in sprite generation is proposed. GME for the sprite generation presented in this paper consists of two stages, feature selection and global motion estimation with selected blocks. First, local object motions are distinguished from the static background. Blocks with local motions are excluded in the subsequent procedure because local object motions would not be helpful to GME and often are even harmful to the exact motion estimation. Note that sprite generation mainly concerns the generation of the static background for a sequence of image frames. To identify local motions, conventional block-based motion estimation is performed for the blocks in the current frame. If it has a greater residual error than a threshold, this block is considered to have an object with local motions and is excluded in the subsequent procedure. Note that a large residual error of a block implies a change in the shape of the object and the block image could not be a part of the static back-ground. The second stage extracts edges in the image excluding blocks selected in the first step and they are used for GME. Experiments show the proposed algorithm performs faster in selected images than existing methods with improved objective/subjective quality.
机译:本文提出了一种新的特征点选择方法,用于在Sprite生成中的全局运动估计(GME)。本文提出的Sprite的GME由两个阶段,特征选择和具有所选块的全局运动估算组成。首先,局部对象动作与静态背景不同。随后的过程中,具有本地运动的块被排除在后续程序中,因为本地对象动作对GME不会有帮助,并且通常对确切的运动估计甚至有害。注意,精灵的产生主要涉及静态背景的一系列图像帧的产生。为了识别本地运动,对当前帧中的块执行传统的基于块的运动估计。如果它具有比阈值更大的剩余错误,则认为该块具有具有本地运动的对象,并且在后续过程中排除。注意,块的大的剩余误差意味着对象的形状的变化,块图像不能是静态背面的一部分。第二阶段在第一步中选择的图像中提取边缘,并且它们用于GME。实验表明,所提出的算法比具有改进的目标/主观质量的现有方法更快地执行速度。

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