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Optical Flow Estimation from Monogenic Phase

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Complex Motion (IWCM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3417))

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Abstract

The optical flow can be estimated by several different methods, some of them require multiple frames some make use of just two frames. One approach to the latter problem is optical flow from phase. However, in contrast to (horizontal) disparity from phase, this method suffers from the phase being oriented, i.e., classical quadrature filter have a predefined orientation in which the phase estimation is correct and the phase error grows with increasing deviation from the local image orientation. Using the approach of the monogenic phase instead, results in correct phase estimates for all orientations if the signal is locally 1D. This allows to estimate the optical flow with sub-pixel accuracy from a multi-resolution analysis with seven filter responses at each scale. The paper gives a short and easy to comprehend overview about the theory of the monogenic phase and the formula for the displacement estimation is derived from a series expansion of the phase. Some basic experiments are presented.

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References

  1. Felsberg, M., Duits, R., Florack, L.: The monogenic scale space on a rectangular domain and its features. International Journal of Computer Vision 64(2-3) (2005)

    Google Scholar 

  2. Felsberg, M., Sommer, G.: The monogenic scale-space: A unifying approach to phase-based image processing in scale-space. Journal of Mathematical Imaging and Vision 21, 5–26 (2004)

    Article  MathSciNet  Google Scholar 

  3. Felsberg, M., Sommer, G.: The monogenic signal. IEEE Transactions on Signal Processing 49, 3136–3144 (2001)

    Article  MathSciNet  Google Scholar 

  4. Felsberg, M.: Disparity from monogenic phase. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 248–256. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Mechler, F., Reich, D.S., Victor, J.D.: Detection and discrimination of relative spatial phase by V1 neurons. Journal of Neuroscience 22, 6129–6157 (2002)

    Google Scholar 

  6. Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995)

    Google Scholar 

  7. Hurt, N.E.: Phase Retrieval and Zero Crossings. Kluwer Academic, Dordrecht (1989)

    MATH  Google Scholar 

  8. Krieger, G., Zetzsche, C.: Nonlinear image operators for the evaluation of local intrinsic dimensionality. IEEE Transactions on Image Processing 5, 1026–1041 (1996)

    Article  Google Scholar 

  9. Oppenheim, A., Lim, J.: The importance of phase in signals. Proc. of the IEEE 69, 529–541 (1981)

    Article  Google Scholar 

  10. Felsberg, M.: On the design of two-dimensional polar separable filters. In: 12th European Signal Processing Conference, Vienna, Austria (2004)

    Google Scholar 

  11. Knutsson, H., Wilson, R., Granlund, G.H.: Anisotropic non-stationary image estimation and its applications: Part I – restoration of noisy images. IEEE Trans. on Communications COM–31, 388–397 (1983)

    Article  Google Scholar 

  12. Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 891–906 (1991)

    Article  Google Scholar 

  13. Jähne, B.: Digital Image Processing. Springer, Berlin (2002)

    MATH  Google Scholar 

  14. Fleet, D.J., Jepson, A.D., Jenkin, M.R.M.: Phase-based disparity measurement. Computer Vision, Graphics, and Image Processing. Image Understanding 53, 198–210 (1991)

    MATH  Google Scholar 

  15. Maimone, M.W., Shafer, S.A.: Modeling foreshortening in stereo vision using local spatial frequency. In: International Robotics and Systems Conference, pp. 519–524. IEEE Computer Society Press, Los Alamitos (1995)

    Google Scholar 

  16. Maki, A., Uhlin, T., Eklundh, J.O.: A direct disparity estimation technique for depth segmentation. In: Proc. 5th IAPR Workshop on Machine Vision Applications, pp. 530–533 (1996)

    Google Scholar 

  17. Schnörr, C.: Variational methods for fluid flow estimation. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 124–145. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Forssén, P.-E., Spies, H.: Multiple Motion Estimation Using Channel Matrices. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 54–65. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Bernd Jähne Rudolf Mester Erhardt Barth Hanno Scharr

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Felsberg, M. (2007). Optical Flow Estimation from Monogenic Phase. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds) Complex Motion. IWCM 2004. Lecture Notes in Computer Science, vol 3417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69866-1_1

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  • DOI: https://doi.org/10.1007/978-3-540-69866-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69864-7

  • Online ISBN: 978-3-540-69866-1

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