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A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes

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Part of the book series: Informatik aktuell ((INFORMAT))

Abstract

We present a new method to simultaneously estimate optical flow fields and parameters of dynamic processes, violating the standard brightness change constraint equation. This technique constitutes a straightforward generalization of the standard brightness constancy assumption. Using TLS estimation the spatiotemporal brightness structure is analyzed in an entirely symmetric way with respect to the spatial and temporal coordinates. We directly incorporate nonlinear brightness changes based upon differential equations of the underlying processes.

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References

  1. Duc, B.: Feature design: applications to motion analysis and identity verification. PhD thesis, École Polytechnique Fédérale de Lausanne (1997)

    Google Scholar 

  2. Florae, L., W. Niessen, and M. Nielsen: The intrinsic structure of optical flow in-corporating measurement duality. Intl. J. Comp. Vis., 27 (3), (1998) 263–286

    Article  Google Scholar 

  3. Hager, G. D., and P. N. Belhumeur: Efficient region tracking with parametric models of geometry and illumination. IEEE PAMI, 20(10), (1998) 1025–1039

    Article  Google Scholar 

  4. Haußecker, H., and B. Jähne: A tensor approach for precise computation of dense displacement vector fields. In ‘Informatik aktuell’, Paulus, E., Wahl, F. M. (Hrsg.), Mustererkennung 1997, Springer-Verlag: Berlin, Heidelberg (1997) 199–208

    Google Scholar 

  5. Haußecker, H., and H. Spies: Motion. In ‘Handbook of Computer Vision and Applications’, Jahne, B., Haußecker, H., and Geißler, P. (Eds.), Academic Press, (1999)

    Google Scholar 

  6. Horn, B. K., and B. G. Schunk: Determining optical flow. AI, 17, (1999) 185–204

    Google Scholar 

  7. Jahne, B., H. Haußecker, H. Scharr, H. Spies, D. Schmundt, and U. Schurr: Study of dynamical processes with tensor-based spatiotemporal image processing techniques. Proc. ECCV ’98 (Vol. 2), Burkhardt, H., and Neumann, B. (Eds.), Springer-Verlag: Berlin, Heidelberg (1998) 322–335

    Google Scholar 

  8. Mühlich, M., and R. Mester: The role of Total Least Squares in motion analysis. Proc. ECCV ’98 (Vol. 2), Burkhardt, H., and Neumann, B. (Eds.), Springer-Verlag: Berlin, Heidelberg (1998) 305–321

    Google Scholar 

  9. Nagel, H.-H.: On a constraint equation for the estimation of displacement rates in image sequences. IEEE PAMI, 11 (1), (1989) 13–30

    Article  MATH  Google Scholar 

  10. Nagel, H.-H., and A. Gehrke: Bildbereichsbasierte Verfolgung von Straßenfahrzeugen durch adaptive Schätzung und Segmentierung von Optischen-Flußfeldern. In ‘Informatik aktuell’, Levi, P., Ahlers, R.-J., May, F., Schanz, M. (Hrsg.), Mustererkennung 1998, Springer-Verlag: Berlin, Heidelberg (1998) 314–321

    Google Scholar 

  11. Negahdaripour, S.: Revised definition of optical flow: integration of radiometric and geometric clues for dynamic scene analysis. IEEE PAMI, 20 (9), (1998) 961–979

    Article  Google Scholar 

  12. Ohta, N.: Optical flow detection using a general noise model. IEICE Trans. Inf. & Syst., Vol. E79-D, No. 7 July (1996) 951–957

    Google Scholar 

  13. Van Huffel, S., and S. Vandewalle: The Total Least Squares Problem: Computational aspects and analysis. SIAM, (1991)

    Google Scholar 

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© 1999 Springer-Verlag Berlin Heidelberg

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Haußecker, H., Garbe, C., Spies, H., Jähne, B. (1999). A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes. In: Förstner, W., Buhmann, J.M., Faber, A., Faber, P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_28

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  • DOI: https://doi.org/10.1007/978-3-642-60243-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66381-2

  • Online ISBN: 978-3-642-60243-6

  • eBook Packages: Springer Book Archive

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