Name
Abstract | ||
|---|---|---|
We propose a sparse aggregation framework for optical flow estimation to overcome the limitations of variational methods introduced by coarse-to-fine strategies. The idea is to compute parametric motion candidates estimated in overlapping square windows of variable size taken in the semi-local neighborhood of a given point. In the second step, a sparse representation and an optimization procedure in the continuous setting are proposed to compute a motion vector close to motion candidates for each pixel. We demonstrate the feasibility and performance of our two-step approach on image pairs and compare its performances with competitive methods on the Middlebury benchmark. |
Year | Venue | Field |
|---|---|---|
2015 | SSVM | Mathematical optimization,Sparse approximation,Algorithm,Optical flow estimation,Parametric statistics,Pixel,Motion estimation,Optical flow,Mathematics,Motion vector |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denis Fortun | 1 | 42 | 5.26 |
| Patrick Bouthemy | 2 | 2675 | 286.70 |
| Charles Kervrann | 3 | 934 | 67.36 |