Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 472-477 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9784901122160 |
| Publikationsstatus | Veröffentlicht - 19 Juli 2017 |
| Veranstaltung | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Dauer: 8 Mai 2017 → 12 Mai 2017 |
Abstract
A novel self-calibration method for estimation of radial lens distortion is proposed. It requires only a single image of a textured plane that may have arbitrary orientation with respect to the camera. A frequency-based approach is used to estimate the perspective and non-linear lens distortions that planar textures are subject to when projected to a camera image plane. The texture is only required to be homogeneous and may exhibit a high amount of stochastic content. For this purpose, we derive the relationship between the local spatial frequencies of the texture and those of the image. In a joint optimization, both the rotation matrix and the radial distortion are subsequently estimated. Results show that with appropriate textures, a mean reprojection error of 9.76 · 10-5 relative to the picture width is achieved. In addition, the method is robust to image corruption by noise.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. S. 472-477 7986903.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Estimation of Radial Distortion Using Local Spectra of Planar Textures
AU - Spitschan, Benjamin
AU - Ostermann, Jörn
PY - 2017/7/19
Y1 - 2017/7/19
N2 - A novel self-calibration method for estimation of radial lens distortion is proposed. It requires only a single image of a textured plane that may have arbitrary orientation with respect to the camera. A frequency-based approach is used to estimate the perspective and non-linear lens distortions that planar textures are subject to when projected to a camera image plane. The texture is only required to be homogeneous and may exhibit a high amount of stochastic content. For this purpose, we derive the relationship between the local spatial frequencies of the texture and those of the image. In a joint optimization, both the rotation matrix and the radial distortion are subsequently estimated. Results show that with appropriate textures, a mean reprojection error of 9.76 · 10-5 relative to the picture width is achieved. In addition, the method is robust to image corruption by noise.
AB - A novel self-calibration method for estimation of radial lens distortion is proposed. It requires only a single image of a textured plane that may have arbitrary orientation with respect to the camera. A frequency-based approach is used to estimate the perspective and non-linear lens distortions that planar textures are subject to when projected to a camera image plane. The texture is only required to be homogeneous and may exhibit a high amount of stochastic content. For this purpose, we derive the relationship between the local spatial frequencies of the texture and those of the image. In a joint optimization, both the rotation matrix and the radial distortion are subsequently estimated. Results show that with appropriate textures, a mean reprojection error of 9.76 · 10-5 relative to the picture width is achieved. In addition, the method is robust to image corruption by noise.
UR - http://www.scopus.com/inward/record.url?scp=85027855724&partnerID=8YFLogxK
U2 - 10.23919/mva.2017.7986903
DO - 10.23919/mva.2017.7986903
M3 - Conference contribution
AN - SCOPUS:85027855724
SP - 472
EP - 477
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Y2 - 8 May 2017 through 12 May 2017
ER -