Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
권호 표지
DOI QR코드

DOI QR Code

Image Data Loss Minimized Geometric Correction for Asymmetric Distortion Fish-eye Lens

비대칭 왜곡 어안렌즈를 위한 영상 손실 최소화 왜곡 보정 기법

  • 조영주 (이화여자대학교 컴퓨터정보통신공학과) ;
  • 김성희 (이화여자대학교 컴퓨터정보통신공학과) ;
  • 박지영 (이화여자대학교 컴퓨터정보통신공학과) ;
  • 손진우 (현대.기아자동차 연구개발 총괄본부 ASV 개발팀) ;
  • 이중렬 (현대.기아자동차 연구개발 총괄본부 ASV 개발팀) ;
  • 김명희 (이화여자대학교 컴퓨터정보통신공학과)
  • Received : 2009.06.30
  • Accepted : 2009.12.07
  • Published : 2010.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

180도 이상의 영역을 획득하는 어안렌즈(fisheye lens)는 최소의 카메라로 최대 시야각을 확보할 수 있는 장점으로 인해 차량 장착 시도가 늘고 있다. 이와 같이 어안렌즈를 통해 시야를 확보하고, 영상센서로 사용하기 위해서는 캘리브레이션 작업이 선행되어야 하며, 운전자에게 현실감 있는 영상을 제공하기 위해서는 이를 이용하여 방사왜곡(radial distortion)에 따른 기하학적인 왜곡 보정이 필요하다. 본 논문에서는 비대칭 왜곡을 가진 180도 이상 화각의 차량용 대각선 어안렌즈를 위해 영상 손실을 최소화하는 왜곡 보정 기법을 제안한다. 왜곡 보정은 왜곡 모델이 포함된 카메라 모델을 설정하고 캘리브레이션 과정을 통해 카메라 파라미터를 구한 후 왜곡이 보정된 뷰를 생성하는 과정으로 이루어진다. 먼저 왜곡모델로서 비선형의 왜곡 형상을 모방한 FOV(Field of View)모델을 사용한다. 또한 비대칭 왜곡렌즈의 경우 운전자의 좌우 시야각 확보에 중점을 두어 수직 화각보다 수평 화각이 크게 설계되었기 때문에 영상의 장축, 단축의 비율을 일치시킨 후 비선형 최적화 알고리즘을 사용하여 카메라 파라미터를 추정한다. 최종적으로 왜곡이 보정된 뷰 생성 시 역방향 사상과 함께 수평, 수직 방향에 대한 왜곡 보정 정도를 제어 가능하도록 함으로써 화각이 180도 이상인 영상에 대해서 핀홀 카메라 모델을 적용하여 2차원 평면으로 영상을 보정하는 경우 발생하는 영상 손실을 최소화하고 시각적 인지도를 높일 수 있도록 하였다.

Keywords

References

  1. D. Scaramuzza and A. Martinelli, "A flexible technique for accurate omnidirectional camera calibration and structure from motion," IEEE International Conference on Computer Vision Systems, 2006.
  2. Besma Roui-Abidi and Chris Broaddus and Vitaliy Orekhov and Mongi Abidi, "A Generalized Full Scale Camera Calibration Method with Optimized Complexity," Proc. Fourth IEEE International Conference Multi-Conference on Systems, 2007.
  3. D.C. Brown, "Close range camera calibration," Photogrammetric Engineering and Remote sensing, vol. 37, pp. 885-566, 1971.
  4. S.B. Kang, "Semi-automatic methods for recovering radial distortion parameters from a single image," Technical Reports Series CRL 97/3, pp. 21, 1997.
  5. 신주홍, 남동환, 권기준, 정순기, "Ellipsoid를 이용한 어안렌즈의 Non-metric 접근 왜곡 보정 기법," HCI 2005, vol. 14, pp.83-89, 2005.
  6. C. Mei and P. Rives, "Single view point omnidirectional camera calibration from planar grids," Proc. IEEE International Conference on Robotics and Automation, pp. 3945-3950, 2007.
  7. R.Y. Tsai, "A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-theshelf TV cameras and lenses," IEEE Journal of Robotics Autom, vol. RA-3, pp. 323-344, 1986.
  8. Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1330-1334, 2000. https://doi.org/10.1109/34.888718
  9. M.A. Abidi and R.O. Eason, "Camera calibration in robot vision," Proc, 4th Scandinavian Conference on Image Analysis, pp. 471-478, 1985.
  10. S. Shah and J.K. Aggarwal, "Intrinsic parameter calibration procedure for a (high distortion) fish-eye lens camera with distortion model and accuracy estimation," Pattern Recognition, vol. 29, pp. 1775-1788, 1996. https://doi.org/10.1016/0031-3203(96)00038-6
  11. S.S. Beauchemin, R. Bajcsy and G. Givaty, "A unified procedure for calibrating intrinsic parameters of fish-eye lenses," Proc, of the Conference on Vision Interface, pp. 272-279, 1999.
  12. H. Bakstein and T. Pajdla, "Panoramic mosaicing with a $183^\circ$ field of view lens," Proc, of the IEEE Workshop on Omnidirectional Vision, pp. 60-67, 2002.
  13. R. Swaminathan and S.K. Nayar, "Non-metric calibration of wide-angle lenses and polycameras," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1172-1178, 2000. https://doi.org/10.1109/34.879797
  14. C. Brauer-Burchardt and K. Voss, "A new algorithm to correct fish-eye- and strong wide-angle-lens-distortion from single images," Proc, of the Conference on Image Processing, pp. 225-228, 2001.
  15. Y. Xiong and K. Turkowski, "Creating image-based VR using a self-calibrating fisheye lens," Proc, of the Conference on Computer Vision and Pattern Recognition, pp. 237-243, 1997.
  16. B. Micusik, T. Pajdla, "Autocalibration and 3D reconstruction with non-central catadioptric cameras," IEEE Internation Conference on Computer Vision and Pattern Recognition, 2004.
  17. J. Heikkila, "Geometric camera calibration using circular control points," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1066-1077, 2000. https://doi.org/10.1109/34.879788
  18. J. P. Barreto and K. Aniilidis, "Fundamental matrix for cameras with radial distortion," International Conference On Computer Vision, pp. 625-632, 2005.
  19. H. Longuet-Higgins, "Acomputer algorithm for reconstructing a scene from two projections," Nature, vol. 293, pp. 133-135, 1981. https://doi.org/10.1038/293133a0
  20. Philip E. Gill and Walter Murray, "Algorithms for the solution of the nonlinear least-squares problem," SIAM Journal on Numerical Analysis, vol. 15, pp. 977-992, 1978. https://doi.org/10.1137/0715063
  21. R. Unnikrishnan and M. Hebert, Fast extrinsic of a laser rangefinder to a camera, Technical Report Robotics Institute, Carnegie Mellon University, 2005.
  22. F. Devernay and O. Faugeras, "Straight lines have to be straight," Machine Vision and Applications, vol. 13, pp. 14-24, 2001. https://doi.org/10.1007/PL00013269