The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Dense estimation of fluid flows
Abstract
In this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from computer vision, originally designed for quasi-rigid motions with stable salient features, are not well adapted in this context. We thus investigate a dedicated minimization-based motion estimator. The cost function to be minimized includes a novel data term relying on an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements. This term is associated with an original second-order div-curl regularization which prevents the washing out of the salient vorticity and divergence structures. The performance of the resulting fluid flow estimator is demonstrated on meteorological satellite images. In addition, we show how the sequences of dense motion fields we estimate can be reliably used to reconstruct trajectories and to extract the regions of high vorticity and divergence.
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Cites
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“In the field of fluid mechanics, experimental imaging techniques now routinely produce various types of videos which constitute a unique source of information for both applied and theoretical studies [1], [43], [46].”
BackgroundRobust regression using iteratively reweighted least-squares.
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The computation of optical flow
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Constrained restoration and the recovery of discontinuities
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Cited by
Robust Point Matching via Vector Field Consensus
Ma, J., Zhao, J., Tian, J., Yuille, A. L., & Tu, Z. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2307478 · Cited by 566
Optical flow modeling and computation: A survey
Fortun, D., Bouthemy, P., & Kervrann, C. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.008 · Cited by 431
LIU, T., & SHEN, L. (2008) · Journal of Fluid Mechanics · doi:10.1017/s0022112008003273 · Cited by 335
Fluid experimental flow estimation based on an optical-flow scheme
Corpetti, T., Heitz, D., Arroyo, G., Mémin, E., & Santa-Cruz, A. (2006) · Experiments in Fluids · doi:10.1007/s00348-005-0048-y · Cited by 266
Self-citationFour-dimensional deformable image registration using trajectory modeling
Castillo, E., Castillo, R., Martinez, J., Shenoy, M., & Guerrero, T. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/1/018 · Cited by 261
Metadata
- Authors
- Corpetti, T., Memin, E., & Perez, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-03
- Volume
- 24
- Issue
- 3
- Pages
- 365-380
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2005067064
- Cited by
- 360
- References
- 30
- FWCI
- 7.49
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
