Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 472-477 |
| Number of pages | 6 |
| ISBN (electronic) | 9784901122160 |
| Publication status | Published - 19 Jul 2017 |
| Event | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 8 May 2017 → 12 May 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 subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Vision and Pattern Recognition
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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. p. 472-477 7986903.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -