Name
Abstract | ||
|---|---|---|
Gradient methods are widely used in the computation of optical flow. The authors discuss extensions of these methods which compute probability distributions of optical flow. The use of distributions allows representation of the uncertainties inherent in the optical flow computation, facilitating the combination with information from other sources. Distributed optical flow for a synthetic image sequence is computed, and it is demonstrated that the probabilistic model accounts for the errors in the flow estimates. The distributed optical flow for a real image sequence is computed |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1109/CVPR.1991.139707 | Maui, HI |
Keywords | Field | DocType |
computer vision,computerised picture processing,probability,errors,flow estimates,gradient methods,optical flow,probabilistic model,probability distributions,real image sequence,synthetic image sequence | Computer vision,Computer science,Flow (psychology),Probability distribution,Artificial intelligence,Statistical model,Real image,Image sequence,Optical flow computation,Optical flow,Computation | Conference |
Volume | Issue | ISSN |
1991 | 1 | 1063-6919 |
Citations | PageRank | References |
190 | 38.76 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eero Simoncelli | 1 | 1316 | 397.43 |
| Adelson | 2 | 3586 | 937.81 |
| Heeger, D.J. | 3 | 1223 | 438.76 |