Physically Based Uid Ow Recovery from Image Sequences

This paper presents an approach to measuring uid ow from image sequences. The approach centers around a motion recovery algorithm that is based on principles from uid mechanics: The algorithm is constrained so that recovered ows observe conservation of mass as well as physically motivated boundary conditions. Results are presented from application of the algorithm to transmittance imagery of uid ows, where the uids contained a contrast medium. In these experiments , the algorithm recovered accurate and precise estimates of the ow. The signiicance of this work is twofold. First, from a theoretical point of view it is shown how information derived from the physical behavior of uids can be used to motivate a ow 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 uid dynamics; other imaged ows that observe compatible constraints might beneet in a similar fashion.

[1]  Stefan Waas,et al.  Optical Measuring Technique For Small Scale Water Surface Waves , 1989, Other Conferences.

[2]  Hans-Gerd Maas,et al.  Feature tracking in 3-D fluid tomography sequences , 1994, Proceedings of 1st International Conference on Image Processing.

[3]  HANS-HELLMUT NAGEL,et al.  On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  R. Leahy,et al.  Computation of 3-D velocity fields from 3-D cine CT images of a human heart. , 1991, IEEE transactions on medical imaging.

[5]  R. Adrian Particle-Imaging Techniques for Experimental Fluid Mechanics , 1991 .

[6]  Shahriar Negahdaripour,et al.  A generalized brightness change model for computing optical flow , 1993, 1993 (4th) International Conference on Computer Vision.

[7]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[8]  J. Spurk Boundary Layer Theory , 2019, Fluid Mechanics.

[9]  J. Michael Fitzpatrick,et al.  The existence of geometrical density-image transformations corresponding to object motion , 1988, Comput. Vis. Graph. Image Process..

[10]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[11]  Dr. M. G. Worster Methods of Mathematical Physics , 1947, Nature.