Two methods for optical flow estimation

Frolkovič, Peter; Kleinová, Viera

  • Proceedings of Equadiff 14, Publisher: Slovak University of Technology in Bratislava, SPEKTRUM STU Publishing(Bratislava), page 331-340

Abstract

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In this paper we describe two methods for optical flow estimation between two images. Both methods are based on the backward tracking of characteristics for advection equation and the difference is on the choice of advection vector field. We present numerical experiments on 2D data of cell nucleus.

How to cite

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Frolkovič, Peter, and Kleinová, Viera. "Two methods for optical flow estimation." Proceedings of Equadiff 14. Bratislava: Slovak University of Technology in Bratislava, SPEKTRUM STU Publishing, 2017. 331-340. <http://eudml.org/doc/294951>.

@inProceedings{Frolkovič2017,
abstract = {In this paper we describe two methods for optical flow estimation between two images. Both methods are based on the backward tracking of characteristics for advection equation and the difference is on the choice of advection vector field. We present numerical experiments on 2D data of cell nucleus.},
author = {Frolkovič, Peter, Kleinová, Viera},
booktitle = {Proceedings of Equadiff 14},
keywords = {Optical flow, advection equation, level-set motion, characteristic curves},
location = {Bratislava},
pages = {331-340},
publisher = {Slovak University of Technology in Bratislava, SPEKTRUM STU Publishing},
title = {Two methods for optical flow estimation},
url = {http://eudml.org/doc/294951},
year = {2017},
}

TY - CLSWK
AU - Frolkovič, Peter
AU - Kleinová, Viera
TI - Two methods for optical flow estimation
T2 - Proceedings of Equadiff 14
PY - 2017
CY - Bratislava
PB - Slovak University of Technology in Bratislava, SPEKTRUM STU Publishing
SP - 331
EP - 340
AB - In this paper we describe two methods for optical flow estimation between two images. Both methods are based on the backward tracking of characteristics for advection equation and the difference is on the choice of advection vector field. We present numerical experiments on 2D data of cell nucleus.
KW - Optical flow, advection equation, level-set motion, characteristic curves
UR - http://eudml.org/doc/294951
ER -

References

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  2. Bruhn, A., Weickert, J., Schnörr, Ch., Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods, , Int. Journal of Comp. Vision. 61(3) (2005), pp. 211-231. 
  3. Horn, B., Schunck, B., Determining optic flow, , Artificial Intelligence, 17(1-3) (1981), pp. 185-203. 
  4. Kleinová, V., Algoritmy extrakcie rýchlostného poľa z postupnosti obrazov, , Diploma thesis, Faculty of Civil Engineering, Slovak University of Technology in Bratislava (2014). 
  5. Lucas, B., Kanade, T., An iterative image registration technique with an application to stereovision, , In Int. Joint Conf. on Artificial Intel, 2 (1981), pp. 674-679. 
  6. Osher, S., Sethian, J.A., Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations, , J. Computational Physics, 79 (1988), pp. 12-49. MR0965860
  7. LeVeque, R. J., Finite Volume Methods for Hyperbolic Problems, , Cambridge University Press, 1 (2002), ISBN: 0521009243. MR1925043
  8. Rouy, E., Tourin, A., A viscosity solutions approach to shape-from-shading, , SIAM J. Num. Anal. 29 (1992), pp. 867-884. MR1163361
  9. Vemuri, B.C., Ye, J., Chen, Y., Leonard, C.M., Image registration via level-set motion: Applications to atlas-based segmentation, , Medical Image Analysis, 7(1) (2003), pp. 1-20. 

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