Advertisement

Fluid Flow Measurement in Thermographic Video Sequences by Wavelet-Multiresolution Optical Flow Estimation

  • Hugo Franco
  • Álvaro Perea
  • Eduardo Romero
  • Daniel Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)

Abstract

Variational Optical Flow estimation models have proven to be highly useful tools for both tracking (rigid) object paths and for calculating motion fields registered in digital video sequences. Specific acquisition techniques, such as infrared thermographic video, allow to carry out further studies of the fluid dynamics for several kind of phenomena. This paper presents a methodological approach to obtain a reliable estimation of the temporal evolution of thermal structures in fluid surfaces using a multiresolution scheme based on the Galerkin-Wavelet Analysis. An appropriate regularizer, adapted for the specific problem herein presented, is also introduced.

Keywords

Optical Flow Connection Coefficients Termographic Video 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)CrossRefGoogle Scholar
  2. 2.
    Aubert, G., Deriche, R., Kornprobst, P.: Computing optical flow via variational techniques. SIAM Journal on Applied Mathematics 60, 156–182 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: combining local and global optic flow methods. Int. J. Comput. Vision 61(3), 211–231 (2005)CrossRefGoogle Scholar
  4. 4.
    Suter, D.: Motion estimation and vector splines. In: CVPR 1994, pp. 939–942 (1994)Google Scholar
  5. 5.
    Ruhnau, P., Stahl, A., Schnorr, C.: On-line variational estimation of dynamical fluid flows with physics-based spatio-temporal regularization. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 444–454. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Francomano, E., Tortorici, A., Calderone, V.: Regularization of optical flow with m-band wavelet transform. Computers and Mathematics with Applications 45, 437–452 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Mémin, E., Pérez, P.: A multigrid approach for hierarchical motion estimation. In: Proc. Int. Conf. Computer Vision, pp. 933–938 (1998)Google Scholar
  8. 8.
    Arigovindan, M., Sühling, M., Jansen, C.P., Hunziker, P., Unser, M.: Full flow/motion-field recovery from pulsed-wave ultrasound doppler data. In: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 213–216 (2006)Google Scholar
  9. 9.
    Corpetti, T., Mémin, E., Pérez, P.: Dense estimation of fluid flows. IEEE Trans. Pattern Anal. Mach. Intell. 24, 365–380 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Corpetti, T., Heitz, D., Arroyo, G., Mémin, E., Santa-Cruz, A.: Fluid experimental flow estimation based on an optical-flow scheme. Int. J. Experiments in Fluid 40, 80–97 (2006)CrossRefGoogle Scholar
  11. 11.
    Varzhanskaya, T.S.: Boundary condition formulation for viscous fluid flow problems. Fluid Dynamics 4, 97–98 (2005)CrossRefGoogle Scholar
  12. 12.
    Chen, L.F., Lin, J.C., Liao, H.Y.M.: Wavelet-based optical flow estimation. ICPR 03, 7068 (2000)Google Scholar
  13. 13.
    Oslick, M., Linscott, I., Maslakovic, S., Twicken, J.: Computing derivatives of scaling functions and wavelets. In: Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, Pittsburgh, PA, USA, pp. 357–360 (1998)Google Scholar
  14. 14.
    Latto, A., Resnikoff, A.H.L., Tenenbaum, A.E.: The evaluation of connection coefficients of compactly supported wavelets. In: Proceedings of the French-USA Workshop on Wavelets and Turbulence. Springer, New York (1991)Google Scholar
  15. 15.
    Bindal, A., Khinast, J.G., Ierapetritou, M.G.: Adaptative multiscale solution of dynamical systems in chemical processes using wavelets. Computer and Chemical Engineering 27, 131–142 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hugo Franco
    • 1
    • 2
  • Álvaro Perea
    • 1
  • Eduardo Romero
    • 2
  • Daniel Rodríguez
    • 1
  1. 1.Depto. de Física Matemática y de FluidosUNEDMadridSpain
  2. 2.Universidad Nacional de ColombiaColombia

Personalised recommendations