Abstract
This paper presents a method for motion estimation of fluid flow in natural scene. Due to drastic brightness changes in images sequence, previous methods based on continuity equation or brightness consistency constraint cannot be applied in this context well. We define Brightness Distribution Matrix (BDM) to present regional brightness. In the initialization of motion field, the BDM consistency between original point and corresponding point is used as a constraint. Towards the incorrect motion vector caused by drastic brightness change, we denoise to the initial motion field by statistical method, and then a novel smoothness constraint is applied to optimization for denoised motion field. The results of natural fluid flow in images show the validity of our method and the obtained motion field can be used to process 3D recovery for fluid flow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yu, M.-S., Li, S.-F.: Dynamic Facial Expression Recognition Based on Optical Flow. Journal of Microelectronics and Computer 22, 113–119 (2005)
Yang, G.-L., Wang, Z.-L., Wang, G.-J., Chen, F.-J.: Facial Expression Recognition Based on Optical Flow for Non-rigid Motion Analysis. Journal of Computer Science 34, 213–229 (2007)
Das Peddada, S., McDevitt, R.: Least Average Residual Algorithm(LARA) for Tracking the Motion of Arctic Sea Ice. IEEE Trans. Geoscience and Remote Sensing 34(4), 915–926 (1996)
Ottenbacher, A., Tomasini, M., Holmund, K., Schmetz, J.: Low-Level Cloud Motion Winds from Metrosat High-Resolution Visible Imagery. Weather and Forecasting 12(1), 175–184 (1997)
Cohen, I., Herlin, I.: Non Uniform Multiresolution Method for Optical Flow and Phase Portrait Models: Environmental Applications. International Journal of Computer Vision 33(1), 29–49 (1999)
Lu, Z., Liao, Q., Pei, J.: A PIV Approach Based on Nonlinear Filter. Journal of Electronics & Information Technology 32(2) (2010)
Shu, X., Kang, S., Long, Y.: Method of medical image registration based on optical flow field. Computer Engineering and Applications 44, 191–198 (2008)
Nogawa, H., Nakajima, Y., Sato, Y.: Acquisition of Symbolic Description from Flow Fields: A New Approach Based on a Fluid Model. IEEE Trans. Pattern Analysis Machine Intelligence 19, 58–63 (1997)
Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of Imaging Understanding Workshop, pp. 121–130 (1981)
Tistarelli, M.: Multiple Constraints for Optical Flow. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 61–70. Springer, Heidelberg (1994)
Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60, 79–87 (1988)
Corpetti, T., Memin, E., Perez, P.: Dense Estimation of fluid flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3) (2002)
Zhou, L., Kambhamettu, C., Goldof, D.B.: Fluid structure and motion analysis from multi-spectrum 2D cloud image sequence. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 744–751 (2000)
Nakajima, Y., Inomata, H., Nogawa, H., Sato, Y., Tamura, S., Okazaki, K., Torii, S.: Physics-based flow estimation of fluids. Pattern Recognition 36, 1203–1212 (2003)
Arnaud, E., Mémin, É., Sosa, R., Artana, G.: A fluid motion estimator for schlieren image velocimetry. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 198–210. Springer, Heidelberg (2006)
Sakaino, H.: Fluid Motion Estimation Method based on Physical Properties of Waves. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)
Fitzpatrick, J.M., Pederson, C.A.: A Method for Calculating Fluid Flow in Time Dependent Density Images. Electronic Imaging 1, 347–352 (1988)
Wang, Y., Zhai, H., Mu, G.: Shape description matrix and its application to color-image retrieval and recognition. Science in China E 47, 159–165 (2004)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision, 321–331 (1987)
DynTex dynamic texture library, http://projects.cwi.nl/dyntex/index.html
Deqing Sun, S., Stefan, R., Michael, J.B.: Secrets of optical flow estimation and their principles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California (2010)
Nilanjan, R.: Computation of fluid and particle motion from a time-sequenced image pair: A Global Outlier Identification Approach. IEEE Transactions on Image Processing 10(20), 2925–2936 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhuang, H., Quan, H. (2012). Fluid Motion Estimation Based on Energy Constraint. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34390-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-34390-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34389-6
Online ISBN: 978-3-642-34390-2
eBook Packages: Computer ScienceComputer Science (R0)