Fast Global Motion Estimation Based on Symmetry Elimination and Difference of Motion Vectors

Zheng Ya-yu; Tian Xiang; Chen Yao-wu

doi:10.3724/SP.J.1146.2008.00176

Volume 31 Issue 4

Dec. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(4): 840-843

Zheng Ya-yu, Tian Xiang, Chen Yao-wu. Fast Global Motion Estimation Based on Symmetry Elimination and Difference of Motion Vectors[J]. Journal of Electronics & Information Technology, 2009, 31(4): 840-843. doi: 10.3724/SP.J.1146.2008.00176

Citation:

Zheng Ya-yu, Tian Xiang, Chen Yao-wu. Fast Global Motion Estimation Based on Symmetry Elimination and Difference of Motion Vectors[J]. Journal of Electronics & Information Technology, 2009, 31(4): 840-843. doi: 10.3724/SP.J.1146.2008.00176

Citation:

PDF( 259 KB)

Fast Global Motion Estimation Based on Symmetry Elimination and Difference of Motion Vectors

doi: 10.3724/SP.J.1146.2008.00176

Received Date: 2008-02-01
Rev Recd Date: 2008-08-12
Publish Date: 2009-04-19

Abstract

Abstract

To reduce the computational complexity of Global Motion Estimation (GME), a fast GME method based on the principle of the symmetry elimination and difference of motion vectors is proposed. First, the translational parameters are estimated by using the technique of the symmetry elimination of motion vectors. And then the transform parameters are estimated by utilizing the principle of the difference of motion vectors and the strategy of the belief judgment. Experimental results on five geometric global motion models and the real video sequence show that the proposed method, compared with the estimation methods based on the Iterative Least Square (ILS) and the Partial Derivative (PD), only need approximately 50% of their computational time while achieving the comparable estimation accuracy.

FullText(HTML)

References(1)

References

Zitova B and Flusser J. Image registration methods: A survey[J].Image and Vision Computing.2003, 21(11):977-1000[2]Liu Z, Zhang Z Y, and Shen L Q. Moving objectsegmentation in the H.264 compressed domain. OpticalEngineering, 2007, 46(1): 017003.[3]You J Y, Liu G Z, and Sun L, et al.. A multiple visual modelsbased perceptive analysis framework for multilevel videosummarization[J].IEEE Trans. on Circuits and Systems forVideo Technology.2007, 17(3):273-285[4]Chen H H, Liang C K, and Peng Y C, et al.. Integration ofdigital stabilizer with video codec for digital video cameras[J].IEEE Trans. on Circuits and Systems for Video Technology.2007, 17(7):801-813[5]Su Y P, Sun M T, and Hsu V. Global motion estimation fromcoarsely sampled motion vector field and the applications[J].IEEE Trans. on Circuits and Systems for Video Technology.2005, 15(2):232-242[6]Rath G B and Makur A. Iterative least squares andcompression based estimations for a four-parameter linearglobal motion model and global motion compensation[J].IEEETrans. on Circuits and Systems for Video Technology.1999,9(7):1075-1099[7]Jozawa H, Kamikura K, and Sagata A, et al.. Two-stagemotion compensation using adaptive global MC and localaffine MC[J].IEEE Trans. on Circuits and Systems for VideoTechnology.1997, 7(1):75-85[8]Demin W and Limin W. Global motion parametersestimation using a fast and robust algorithm[J].IEEE Trans. onCircuits and Systems for Video Technology.1997, 7(5):823-826[9]Dufaux F and Mosheni F. Motion estimation techniques fordigital TV: A review and a new contribution[J].Proc. IEEE.1995, 83(6):858-876[10]Huang Y R, Kuo C M, and Kuo C L. Efficient global motionestimation algorithm using recursive least squares. OpticalEngineering, 2006, 45(5): 057003.

Relative Articles

Supplements(0)

Cited By

Proportional views