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1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'97)
Physically based fluid flow recovery from image sequences
Puerto Rico
June 17-June 19
ISBN: 0-8186-7822-4
Richard P. Wildes, David Sarnoff Research Center, Inc.
Michael J. Amabile, David Sarnoff Research Center, Inc.
Ann-Marie Lanzillotto, David Sarnoff Research Center, Inc.
Tzong-Shyng Leu, David Sarnoff Research Center, Inc.
This paper presents an approach to measuring fluid flow from image sequences. The approach centers around a motion recovery algorithm that is based on principles from fluid mechanics: The algorithm is constrained so that recovered flows observe conservation of mass as well as physically motivated boundary conditions. Empirical results are presented from application of the algorithm to fluid flows captured via transmittance imagery (i.e., radiographs). In these experiments, fluids seeded with tracers were driven through simple physical systems. The significance of this work is twofold. First, from a theoretical point of view it is shown how information derived from the physical behavior of fluids can be used to motivate a flow recovery algorithm. Second, from an applications point of view the developed algorithm can be used to augment the tools that are available for the measurement of fluid dynamics; other imaged flows that observe compatible constraints might benefit in a similar fashion.
Index Terms:
low-level and physics-based vision, image sequence analysis, applications
Citation:
Richard P. Wildes, Michael J. Amabile, Ann-Marie Lanzillotto, Tzong-Shyng Leu, "Physically based fluid flow recovery from image sequences," cvpr, pp.969, 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'97), 1997
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