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3 November 2005 A new global motion estimation algorithm
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Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 604422 (2005) https://doi.org/10.1117/12.655278
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Abstract
GME (Global Motion Estimation) is an important tool widely used in computer vision, video processing, and other fields. In this paper, we propose an efficient, robust, and fast method for the estimation of global motion from compressed image sequences. With regard to global motion models, we adopt six-parameter affine model because of its reasonable tradeoff between complexity and accuracy. In order to improve accuracy and computational efficiency of global motion estimation, we present a new algorithm for segmentation between background and foreground. Then, motion vectors samples associated with background macroblocks are selected to estimate motion model parameters. Lastly, according to the statistics of estimated error, some sample pairs may be rejected as outliers to compensate further for the fact that some of the samples obtained from the P-frame motion vectors are highly erroneous and the parameters may be refined by estimating from the remaining data. The extensive experiments show that the proposed method is efficient and robust in terms of both computational complexity and accuracy.
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Zhenming Zhang, Feng Wang, Guangxi Zhu, Lei Xie, and Jingbo Gao "A new global motion estimation algorithm", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 604422 (3 November 2005); https://doi.org/10.1117/12.655278
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