Constrained, globally optimal, multi-frame motion estimation

被引:22
作者
Farsiu, Sina [1 ]
Elad, Michael [1 ]
Milanfar, Peyman [1 ]
机构
[1] Univ Calif Santa Cruz, Dept Elect Engn, Santa Cruz, CA 95064 USA
来源
2005 IEEE/SP 13TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP), VOLS 1 AND 2 | 2005年
关键词
D O I
10.1109/SSP.2005.1628814
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
We address the problem of estimating the relative motion between the frames of a video sequence. In comparison with the commonly applied pairwise image registration methods, we consider global consistency conditions for the overall multi-frame motion estimation problem, which is more accurate. We review the recent work on this subject and propose an optimal framework, which can apply the consistency conditions as both hard constraints in the estimation problem, or as soft constraints in the form of stochastic (Bayesian) priors. The framework is applicable to virtually any motion model and enables us to develop a robust approach, which is resilient against the effects of outliers and noise. The effectiveness of the proposed approach is confirmed by a super-resolution application on synthetic and real data sets.
引用
收藏
页码:1313 / 1318
页数:6
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