Name
Title | ||
|---|---|---|
Fast and Robust Variational Optical Flow for High-Resolution Images Using SLIC Superpixels |
Abstract | ||
|---|---|---|
We show how pixel-based methods can be applied to a sparse image representation resulting from a superpixel segmentation. On this sparse image representation we only estimate a single motion vector per superpixel, without working on the full-resolution image. This allows the accelerated processing of high-resolution content with existing methods. The use of superpixels in optical flow estimation was studied before, but existing methods typically estimate a dense optical flow field --- one motion vector per pixel --- using the full-resolution input, which can be slow. Our novel approach offers important speed-ups compared to dense pixel-based methods, without significant loss of accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25903-1_18 | ACIVS |
Keywords | Field | DocType |
SLIC superpixels,Segmentation,Optical flow | Computer vision,Pattern recognition,Computer science,Segmentation,Sparse image,Optical flow estimation,Pixel,Artificial intelligence,Optical flow,Superpixel segmentation,Motion vector | Conference |
Volume | ISSN | Citations |
9386 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Donné | 1 | 19 | 4.30 |
| Jan Aelterman | 2 | 80 | 11.46 |
| Bart Goossens | 3 | 220 | 25.94 |
| Wilfried Philips | 4 | 50 | 4.97 |