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http://dx.doi.org/10.7848/ksgpc.2019.37.4.267

Conversion of Camera Lens Distortions between Photogrammetry and Computer Vision  

Hong, Song Pyo (Dept. of GIS Engineering, Namseoul University)
Choi, Han Seung (Mapping & Localization, Naver Labs Corp.)
Kim, Eui Myoung (Department of Spatial Information Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.4, 2019 , pp. 267-277 More about this Journal
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.
Keywords
Photogrammetry; Computer Vision; Interior Orientation Parameters; Lens Distortions; Epipolar Images;
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Times Cited By KSCI : 4  (Citation Analysis)
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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.   DOI
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.   DOI
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)   DOI
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)   DOI
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)   DOI
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)   DOI
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)   DOI
13 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).
14 Luhmann, T., Robson, S., Kyle, S., and Harley, I. (2011), Close Range Photogrammetry Principles, techniques and applications, Whittles Publishing, Dunbeath, Highland.
15 McGlone, J.C. (2013), Manual of Photogrammetry: 6th Edition, American Society Photogrammetry and Remote Sensing (ASPRS), Bethesda, MD.
16 Mikhail, E.M., Bethel, J.S., and McGlone, J.C. (2001), Introduction to Modern Photogrammetry, John Wiley & Sons Inc., New York, N.Y.
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.   DOI