Abstract
Analyzing fluid motion is essential in number of domains and can rarely be handled using generic computer vision techniques. In this particular application context, we address two distinct problems. First we describe a dedicated dense motion estimator. The approach relies on constraints issuing from fluid motion properties and allows us to recover dense motion fields of good quality. Secondly, we address the problem of analyzing such velocity fields. We present a kind of motion-based segmentation relying on an analytic representation of the motion field that permits to extract important quantities such as singularities, stream-functions or velocity potentials. The proposed method has the advantage to be robust, simple, and fast.
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References
A. Amini. A scalar function formulation for optical flow. In Proc. Europ. Conf. Computer Vision, pages 125–131, 1994.
V.I. Arnold. Ordinary differential equations. MIT Press, 1990.
M. Basseville. Distance measure for signal processing and pattern recognition. Signal Processing, (18):349–369, 1989.
D. Béréziat, I. Herlin, and L. Younes. A generalized optical flow constraint and its physical interpretation. In Proc. Conf. Comp. Vision Pattern Rec., volume 2, pages 487–492, Hilton Head Island, South Carolina, USA, 2000.
M. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63(1):75–104, 1996.
A. J. Chorin. Vorticity and turbulence. Applied Math. Sciences 103, Springer Verlag.
T. Corpetti, E. Mémin, and P. Pérez. Dense estimation of fluide flows. IEEE Trans on Pattern Analysis and Machine Intelligence, 24(3), March 2002.
T. Corpetti, E. Mémin, and P. Pérez. Extraction of singular points from dense motion fields: an analytic approach. Journal of Math. Imag. and Vision, 2002. Accepted under minor revisions.
S. Das Peddada and R. McDevitt. Least average residual algorithm (LARA) for tracking the motion of artic sea ice. IEEE trans. on Geosciences and Remote sensing, 34(4):915–926, 1996.
J.M. Fitzpatrick. The existence of geometrical density-image transformations corresponding to object motion. Comput. Vision, Graphics, Image Proc., 44(2):155–174, 1988.
R.M. Ford, R. Strickland, and B. Thomas. Image models for 2-d flow visualization and compression. Graph. Mod. Image Proc., 56(1):75–93, 1994.
S. Gupta and J. Prince. Stochastic models for div-curl optical flow methods. Signal Proc. Letters, 3(2):32–34, 1996.
B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185–203, 1981.
P. Kornprobst, R. Deriche, and G. Aubert. Image sequence analysis via partial differential equations. Journal of Mathematical Imaging and Vision, 11(1):5–26, September 1999.
R. Larsen, K. Conradsen, and B.K. Ersboll. Estimation of dense image flow fields in fluids. IEEE trans. on Geoscience and Remote sensing, 36(1):256–264, 1998.
E. Mémin and P. Pérez. Dense estimation and object-based segmentation of the optical flow with robust techniques. IEEE Trans. Image Processing, 7(5):703–719, 1998.
E. Mémin and P. Pérez. Hierarchical estimation and segmentation of dense motion fields. Int. J. Computer Vision, 46(2):129–155, February 2002.
A. Nomura, H. Miike, and K. Koga. Field theory approach for determining optical flow. Pattern Recognition Letters, 12(3):183–190, 1991.
A. Ottenbacher, M. Tomasini, K. Holmund, and J. Schmetz. Low-level cloud motion winds from Meteosat high-resolution visible imagery. Weather and Forecasting, 12(1):175–184, 1997.
B.G. Schunk. The motion constraint equation for optical flow. In Proc. Int. Conf. Pattern Recognition, pages 20–22, Montreal, 1984.
S.M. Song and R.M. Leahy. Computation of 3D velocity fields from 3D cine and CT images of human heart. IEEE trans. on medical imaging, 10(3):295–306, 1991.
D. Suter. Motion estimation and vector splines. In Proc. Conf. Comp. Vision Pattern Rec., pages 939–942, Seattle, USA, June 1994.
R. Wildes, M. Amabile, A.M. Lanzillotto, and T.S. Leu. Physically based fluid flow recovery from image sequences. In Proc. Conf. Comp. Vision Pattern Rec. pages 969–975, 1997.
L. Zhou, C. Kambhamettu, and D. Goldgof. Fluid structure and motion analysis from multi-spectrum 2D cloud images sequences. In Proc. Conf. Comp. Vision Pattern Rec., volume 2, pages 744–751, Hilton Head Island, South Carolina, USA, 2000.
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Corpetti, T., Mémin, É., Pérez, P. (2002). Dense Motion Analysis in Fluid Imagery. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47969-4_45
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DOI: https://doi.org/10.1007/3-540-47969-4_45
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