DOI QR코드

DOI QR Code

Conversion of Camera Lens Distortions between Photogrammetry and Computer Vision

사진측량과 컴퓨터비전 간의 카메라 렌즈왜곡 변환

  • Received : 2019.08.19
  • Accepted : 2019.08.28
  • Published : 2019.08.31

Abstract

Photogrammetry and computer vision are identical in determining the three-dimensional coordinates of images taken with a camera, but the two fields are not directly compatible with each other due to differences in camera lens distortion modeling methods and camera coordinate systems. In general, data processing of drone images is performed by bundle block adjustments using computer vision-based software, and then the plotting of the image is performed by photogrammetry-based software for mapping. In this case, we are faced with the problem of converting the model of camera lens distortions into the formula used in photogrammetry. Therefore, this study described the differences between the coordinate systems and lens distortion models used in photogrammetry and computer vision, and proposed a methodology for converting them. In order to verify the conversion formula of the camera lens distortion models, first, lens distortions were added to the virtual coordinates without lens distortions by using the computer vision-based lens distortion models. Then, the distortion coefficients were determined using photogrammetry-based lens distortion models, and the lens distortions were removed from the photo coordinates and compared with the virtual coordinates without the original distortions. The results showed that the root mean square distance was good within 0.5 pixels. In addition, epipolar images were generated to determine the accuracy by applying lens distortion coefficients for photogrammetry. The calculated root mean square error of y-parallax was found to be within 0.3 pixels.

사진측량과 컴퓨터비전 분야는 카메라에서 촬영된 영상에서 3차원 좌표를 결정하는 것은 동일하지만 두 분야는 카메라 렌즈왜곡 모델링 방법과 카메라 좌표계의 차이점으로 인하여 서로 간에 직접적인 호환이 어렵다. 일반적으로 드론 영상의 자료처리는 컴퓨터비전 기반의 소프트웨어를 이용하여 번들블록조정을 수행한 후 지도제작을 위해서 사진측량 기반의 소프트웨어로 도화를 수행하게 된다. 이때 카메라 렌즈왜곡의 모델을 사진측량에서 사용하는 수식으로 변환해야 하는 문제에 직면하게 된다. 이에 본 연구에서는 사진측량과 컴퓨터비전에서 사용되는 좌표계와 렌즈왜곡 모델식의 차이점에 대하여 기술하고 이를 변환하는 방법론을 제안하였다. 카메라 렌즈왜곡 모델의 변환식의 검증을 위해서 먼저 렌즈왜곡이 없는 가상의 좌표에 컴퓨터비전 기반의 렌즈왜곡 모델을 이용하여 렌즈왜곡을 부여하였다. 그리고 나서 렌즈왜곡이 부여된 사진좌표를 이용하여 사진측량 기반의 렌즈왜곡 모델을 이용하여 왜곡계수를 결정한 후 사진좌표에서 렌즈왜곡을 제거하여 원래의 왜곡이 없는 가상좌표와 비교하였다. 그 결과 평균제곱근거리가 0.5픽셀 이내로 양호한 것으로 나타났다. 또한 사진측량용 렌즈왜곡 계수를 적용하여 정밀도화 가능여부를 판단하기 위해서 에피폴라 영상을 생성하였다. 생성된 에피폴라 영상에서 y-시차의 평균제곱근오차가 계산한 결과 0.3픽셀 이내로 양호하게 나타났음을 알 수 있었다.

Keywords

References

  1. Bianco, S., Ciocca, G., and Marelli, D. (2018), Evaluating the performance of structure from motion pipelines, Journal of Imaging, Vol. 4, No. 98, pp. 1-18. https://doi.org/10.1117/1.JMI.4.4.041301
  2. Bouguet, J.Y. (2015), Camera calibration toolbox for matlab, Caltech Vision, URL: http://www.vision.caltech.edu/bouguetj/calib_doc (last date accessed: 5 August 2019).
  3. Drap, P. and Lefevre, J. (2016), An exact formula for calculating inverse radial lens distortions, Sensors, Vol. 16, No. 807, pp. 1-18. https://doi.org/10.1109/JSEN.2016.2616227
  4. Hartley, R. and Zisserman, A. (2003), Multiple View Geometry in Computer Vision: 2nd Edition, Cambridge university press, Cambridge, Cambridgeshire.
  5. Hong, I.Y. (2016), Image processing for micro UAV with open source software, Journal of the Korean Cartographic Association, Vol. 16, No. 3, pp. 139-151. (in Korean with English abstract) https://doi.org/10.16879/jkca.2016.16.3.139
  6. Hong, S.P., Choi, H.S., and Kim, E.M. (2019), Single photo resection using cosine law and three-dimensional coordinate transformation, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 37, No. 3, pp. 189-198. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2019.37.3.189
  7. Kaehler, A. and Bradski, G. (2016), Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media Inc., Sebastopol, California.
  8. Kim, E.M., Choi, H.S., and Hong, S.P. (2018). Generation of epipolar image from drone image using direction cosine. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 4, pp. 271-277. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.4.271
  9. Kim, E.M., Choi, H.S., and Park, J.H. (2017), Analysis of applicability of orthophoto using 3d mesh on aerial image with large file size, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 3, pp. 155-166. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2017.35.3.155
  10. Kim, T.J., Lim, P.C., Son, J.W., and Seo, S.H. (2019), Feasibility study of 1:1,000 scale map generation using various UAVs and processing SW, Proceedings of Journal of Korean Society for Geospatial Information System, Korean Society for Geospatial Information Science, 31-1 May, Busan, Korea, pp. 15-16. (in Korean)
  11. Lee, J.O., Sung, S.M., and Kim, D.P. (2019), Accuracy assessment of stereo plotting with UAV Images, Proceedings of Journal of Korean Society for Geospatial Information System, Korean Society for Geospatial Information Science, 31-1 May, Busan, Korea, pp. 142-143. (in Korean)
  12. Lim, S.B., Seo, C.W., and Yun, H.C. (2015), Digital map updates with UAV photogrammetric methods, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 5, pp. 397-405. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2015.33.5.397
  13. Luhmann, T., Robson, S., Kyle, S., and Harley, I. (2011), Close Range Photogrammetry Principles, techniques and applications, Whittles Publishing, Dunbeath, Highland.
  14. McGlone, J.C. (2013), Manual of Photogrammetry: 6th Edition, American Society Photogrammetry and Remote Sensing (ASPRS), Bethesda, MD.
  15. Mikhail, E.M., Bethel, J.S., and McGlone, J.C. (2001), Introduction to Modern Photogrammetry, John Wiley & Sons Inc., New York, N.Y.
  16. Pix4Dmapper. (2019), How are the internal and external camera parameters defined?, Pix4D, URL: https://support.pix4d.com/hc/en-us/articles/202559089-How-are-the-Internal-and-External-Camera-Parameters-defined (last date accessed: 27 August 2019).
  17. Rabiu, L. and Waziri, D.A. (2014), Digital orthophoto generation with aerial photograph, Academic Journal of Interdisciplinary Studies, Vol. 3, No. 7, pp. 133-141.
  18. Verhoeven, G., Doneus, M., Briese, C., and Vermeulen, F. (2012), Mapping by matching: a computer vision based approach to fast and accurate georeferencing of archaeological aerial photographs, Journal of Archaeological Science, Vol. 39, No. 7, pp. 2060-2070. https://doi.org/10.1016/j.jas.2012.02.022