Name
Abstract | ||
|---|---|---|
This paper focuses on Fluid Motion-Field Estimation from video data, which is a useful but challenging problem in environmental monitoring. Rivers are often monitored by flashy hydrographs that exhibit characteristic response times ranging from minutes to hours. In order to estimate the river discharge during a flush flood event, the temporary motion vector field of the river surface is needed. This paper presents a new approach in statistical estimation of fluid flow that calculates a local flow probability distribution function in the frequency domain. Our work improves upon the inefficiencies of spatial estimation of the auto-regressive STAR model and converts motion estimation into a restoration problem, where the local field can be computed fast in the frequency domain, while various natural constraints can be taken into account within the inversion strategy of the motion estimation process. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-14249-4_16 | ADVANCES IN VISUAL COMPUTING (ISVC 2014), PT 1 |
Field | DocType | Volume |
Motion field,Computer science,Artificial intelligence,Motion estimation,Hydrograph,Image restoration,Frequency domain,Computer vision,Simulation,Algorithm,Fluid dynamics,Probability density function,Motion vector | Conference | 8887 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantia Moirogiorgou | 1 | 1 | 3.06 |
| Michalis E. Zervakis | 2 | 94 | 17.52 |
| Andreas Savakis | 3 | 377 | 41.10 |
| Ioannis Sibetheros | 4 | 0 | 0.34 |