Name
Abstract | ||
|---|---|---|
Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WACV.2016.7477565 | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | Field | DocType |
OCPAD,occluded checkerboard pattern detector,camera calibration techniques,geometry,outer image regions,low-resolution images,high lens distortion,graph representation,graph matching | Distortion (optics),Field of view,Computer vision,Reprojection error,Pattern recognition,Checkerboard,Computer science,Lens (optics),Camera resectioning,Artificial intelligence,Distortion,Detector | Conference |
ISSN | Citations | PageRank |
2472-6737 | 0 | 0.34 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Fürsattel | 1 | 0 | 0.34 |
| Sergiu Dotenco | 2 | 14 | 2.28 |
| Simon Placht | 3 | 14 | 2.35 |
| Michael Balda | 4 | 40 | 4.35 |
| Andreas K. Maier | 5 | 560 | 178.76 |
| Christian Riess | 6 | 535 | 42.27 |