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Motion Compensation Algorithm Based on Color Orientation Codes and Covariance Matching

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6425))

Abstract

Regions extraction and matching are two key steps in image motion compensation. This paper applies the skills of covariance matching and information entropy to motion compensation to improve its performance. First, color orientation codes are employed to compute the information entropy of an image region and detect the sub-block regions automatically. Secondly, covariance matrices are used to match sub-blocks between current frame and previous frame. Finally, we gain the global motion parameters by affine motion model. Experimental results show that the proposed algorithm can detect and compensate global motion in indoor and outdoor environment and has outstanding result than traditional histogram matching.

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References

  1. Vella, F., Castorina, A., Mancuso, M.: Digital image stabilization by adaptive block motion vectors filtering. IEEE Trans. on Consumer Electronics 48(3), 796–801 (2002)

    Article  Google Scholar 

  2. Morimoto, C., Chellappa, R.: Fast electronic digital image stabilization. In: IEEE ICPR 1996, pp. 284–288 (1996)

    Google Scholar 

  3. Sung, J.K., Sung, H.L., Seung, W.J.: Fast digital image stabilize based on gray-coded bit- plane matching. IEEE Trans. on Consumer Electronics 45(3), 598–603 (1999)

    Article  Google Scholar 

  4. Pourreza, H.R., Rahmati, M., Behazin, F.: Weighted Multiple Bit-Plane Matching, a Simple and Efficient Matching Criterion for Electronic Digital Image Stabilizer Application. In: WSCG 2002, pp. 37–40 (2002)

    Google Scholar 

  5. Shi, J.B., Tomasi, C.: Good points to track. In: IEEE CVPR, pp. 593–600 (1994)

    Google Scholar 

  6. Censi, A., Fusiello, A., Roberto, V.: Image stabilization by featurestracking. Technical report, University of Udine, Machine Vision Laboratory, Dept. of Mathematics and Informatics (1998)

    Google Scholar 

  7. Gholipour, A., Kehtarnavaz, N., Yousefi, S., Gopinath, K., Briggs, R.: Symmetric deformable image registration via optimization of information theoretic measures. Image and Vision Computing 28(6), 965–975 (2010)

    Article  Google Scholar 

  8. Cagnazzo, M., Antonini, M., Barlaud, M.: Mutual information-based context quantization. Signal Processing: Image Communication 25(1), 64–74 (2010)

    Google Scholar 

  9. Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: European Conf. Computer Vision, Graz, Austria, vol. 2, pp. 589–600 (2006)

    Google Scholar 

  10. Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on Lie algebra. In: IEEE Conf. Computer Vision and Pattern Recognition, New York, vol. 1, pp. 728–735 (2006)

    Google Scholar 

  11. Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on Riemannian manifolds. IEEE Trans. Pattern Anal. Machine Intelligence 30, 1713–1727 (2008)

    Article  Google Scholar 

  12. Porikli, F., Kocak, T.: Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework. In: IEEE Conf. Advanced Video and Signal Based Surveilance, p. 107 (2006)

    Google Scholar 

  13. Pang, Y., Yuan, Y., Li, X.: Gabor-Based Region Covariance Matrices for Face Recognition. IEEE Trans. Circuits and System for Video Technology 18(7), 989–993 (2008)

    Article  Google Scholar 

  14. Domae, Y., Kaneko, S., Tanaka, T.: Robust tracking based on orientation code matching under irregular conditions. In: Proc. of SPIE, pp. 60510S-1–60510S-9 (2005)

    Google Scholar 

  15. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn., pp. 335–339. Prentice Hall, Englewood Cliffs (2002)

    Google Scholar 

  16. Förstner, W., Moonen, B.: A metric for covariance matrices. Technical report, Dept. of Geodesy and Geoinformatics, Stuttgart University (1999)

    Google Scholar 

  17. Kang, E., Cohen, I., Medioni, G.: Robust Affine Motion Estimation in Joint Image Space Using Tensor Voting. In: 16th International Conference on Pattern Recognition (2002)

    Google Scholar 

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhang, X., Ding, W., Niu, P. (2010). Motion Compensation Algorithm Based on Color Orientation Codes and Covariance Matching. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16587-0_34

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  • DOI: https://doi.org/10.1007/978-3-642-16587-0_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16586-3

  • Online ISBN: 978-3-642-16587-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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