A new energy-based method for 3D motion estimation of incompressible PIV flows

Computer Vision and Image Understanding

Volumn 113, Issue 7, 2009, Pages 802-810

  Alvarez, L ^a   Castano C A ^a   Garcia M ^a   Krissian, K ^a   Mazorra, L ^a   Salgado, A ^a   Sanchez J ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

Incompressible flows; Optical flow; Partial differential equation; Particle Image Velocimetry; Variational methods

Indexed keywords

2D IMAGES; 3D FLOW; 3D MOTION ESTIMATION; ENERGY-BASED METHODS; ESTIMATION TECHNIQUES; FLOW MOTION; FLUID ANALYSIS; LARGE CLASS; MINIMIZATION PROBLEMS; MOVING PARTICLES; NUMERICAL ALGORITHMS; NUMERICAL EXPERIMENTS; PARTICLE IMAGE VELOCIMETRY; PHYSICAL CONSTRAINTS; SYNTHETIC AND REAL DATA; TECHNOLOGICAL ADVANCES; THEORETICAL POINTS; VARIATIONAL METHODS;

COMPUTATIONAL FLUID DYNAMICS; COMPUTER VISION; EQUATIONS OF MOTION; ESTIMATION; FLOW VISUALIZATION; INCOMPRESSIBLE FLOW; MOTION ESTIMATION; OPTICAL FLOWS; PARTIAL DIFFERENTIAL EQUATIONS; VELOCIMETERS; VELOCITY MEASUREMENT;

THREE DIMENSIONAL;

EID: 67349101851     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2009.01.005     Document Type: Article

References (29)

1. Raffel M., Willert C., and Kompenhans J. Particle image velocimetry. A Practical Guide (1998), Springer Verlag
2. Hinsch K.D. Three-dimensional particle velocimetry. Meas. Sci. Tech. 6 (1995) 742-753
3. Royer H., and Stanislas M. Stereoscopic and holographic approaches to get the third velocity component in PIV. Karman Inst. Fluid Dyn. Lect. Ser. 3 (1996) I1-I56
4. Scarano F. Iterative image deformation methods in PIV. Meas. Sci. Technol. 13 (2002) R1-R19
5. Corpetti T., Heitz D., Arroyo G., Mémin E., and Santa-Cruz A. Fluid experimental flow estimation based on an optical-flow scheme. Exp. Fluids 40 1 (2006) 80-97
6. Aubert G., and Kornprobst P. Mathematical problems in image processing: partial differential equations and the calculus of variations. Applied Mathematical Sciences. second ed. vol. 147 (2006), Springer-Verlag
7. B. Horn, B. Schunck, Determining Optical Flow, MIT Artificial Intelligence Laboratory, 1980, pp. 185-203.
8. Beauchemin S.S., and Barron J.L. The computation of optical flow. ACM Comput. Surv. 27 3 (1995) 433-467
9. Barron J., Fleet D., and Beauchemin S. Performance of optical flow techniques. IJCV 12 1 (1994) 43-77
10. Alvarez L., Weickert J., and Sánchez J. Reliable estimation of dense optical flow fields with large displacements. Int. J. Comput. Vis. 39 1 (2000) 41-56
11. Alvarez L., Deriche R., Papadopoulo T., and Sánchez J. Symmetrical dense optical flow estimation with occlusions detection. Int. J. Comput. Vis. 75 3 (2007) 371-386
12. Aubert G., Deriche D., and Kornprobst P. Computing optical flow via variational techniques. SIAM J. Appl. Math. 60 1 (1999) 152-182
13. Bruhn A., Weickert J., Kohlberger T., and Schnörr C. A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int. J. Comput. Vis. 70 3 (2006) 257-277
14. Kohlberger T., Schnörr C., Bruhn A., and Weickert J. Domain decomposition for variational optical flow computation. IEEE Trans. Image Process. 14 8 (2005) 1125-1137
15. Weickert J., and Schnörr C. A theoretical framework for convex regularizers in PDE-based computation of image motion. Int. J. Comput. Vis. 45 3 (2001) 245-264
16. Ruhnau P., Stahl A., and Schnörr C. Variational estimation of experimental fluid flows with physics-based spatio-temporal regularization. Meas. Sci. Technol. 18 (2007) 755-763
17. Ruhnau P., and Schnörr C. Optical Stokes Flow Estimation: an Imaging-Based Control Approach. Experiments in Fluids 42 (2007) 61-78
18. Ruhnau P., Guetter C., Putze T., and Schnörr C. A variational approach for particle tracking velocimetry. Meas. Sci. Technol. 16 (2005) 1449-1458
19. Ruhnau P., Kohlberger T., Nobach H., and Schnörr C. Variational optical flow estimation for particle image velocimetry. Exp. Fluids 38 (2005) 21-32
20. Corpetti T., Mémin E., and Pérez P. Extraction of singular points from dense motion fields: an analytic approach. J. Math. Imag. Vis. 19 3 (2003) 175-198
21. Corpetti T., Mémin E., and Pérez P. Dense estimation of fluid flows. IEEE Trans. Pattern Anal. Mach. Intell. 24 3 (2002) 365-380
22. J. Yuan, P. Ruhnau, E. Mémin, C. Schnörr, Discrete orthogonal decomposition and variational fluid flow estimation, in: Springer Berlin/Heidelberg (Ed.), Lecture Notes in Compute Science (Scale Space 2005), vol. 3459, 2005, pp. 267-278.
23. L. Alvarez, C. Castan, M. García, K. Krissian, L. Mazorra, A. Salgado, J. Sánchez, 3D motion estimation using a combination of correlation and variational methods for PIV, in: R. Moreno Díaz, F. Pichler, A. Quesada Arencibia (Eds.), Computer Aided Systems Theory - EUROCAST 2007, vol. 4739, in: Lecture Notes in Computer Science, Springer Verlag, Heidelberg, 2007, pp. 612-620.
24. L. Alvarez, C. Castano, M. García, K. Krissian, L. Mazorra, A. Salgado, J. Sánchez, A variational approach for 3D motion estimation of incompressible PIV flows, in: Springer Berlin/Heidelberg (Ed.), Scale Space and Variational Methods in Computer Vision, vol. 4485 of series Lecture Notes in Computer Science, ISBN 978-3-540-72822-1, 837-847, 2007.
25. Galdi G. An introduction to the mathematical theory of the Navier-Stokes equation (1994), Springer Verlag (vol. I-II)
26. R. Temam, Navier-Stokes equations, North-Holland, 1979.
27. Temam R. Navier-Stokes equations and Nonlinear functional analysis (1983), SIAM
28. White F. Viscous Fluid Flow (2006), McGraw-Hill
29. P. Parnaudeau, J. Carlier, D. Heitz, E. Lamballais, Experimental and numerical studies of the flow over a circular cylinder at Reynolds number 3900, Phys. Fluids 20(8) (2008) 085101, doi:10.1063/1.2957018, URL: http://link.aip.org/link/?PHF/20/085101/1.