Converting Underwater Imaging into Imaging in Air

Tim Dolereit, Arjan Kuijper

Abstract

The application of imaging devices in underwater environments has become a common practice. Protecting the camera’s constituent electric parts against water leads to refractive effects emanating from the water-glass-air transition of light rays. These non-linear distortions can not be modeled by the pinhole camera model. For our new approach we focus on flat interface systems. By handling refractive effects properly, we are able to convert the problem to imaging conditions in air. We show that based on the location of virtual object points in water, virtual parameters of a camera following the pinhole camera model can be computed per image ray. This enables us to image the same object as if it was situated in air. Our novel approach works for an arbitrary camera orientation to the refractive interface. We show experimentally that our adopted physical methods can be used for the computation of 3D object points by a stereo camera system with much higher precision than with a naive in-situ calibration.

References

  1. Agrawal, A., Ramalingam, S., Taguchi, Y., and Chari, V. (2012). A theory of multi-layer flat refractive geometry. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3346 - 3353.
  2. Bartlett, A. A. (1984). Note on a common virtual image. American Journal of Physics, 52(7):640 - 643.
  3. Beall, C., Lawrence, B. J., Ila, V., and Dellaert, F. (2010). 3D reconstruction of underwater structures.
  4. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4418 - 4423.
  5. Brandou, V., Allais, A. G., Perrier, M., Malis, E., Rives, P., Sarrazin, J., and Sarradin, P. M. (2007). 3D reconstruction of natural underwater scenes using the stereovision system IRIS. In OCEANS 2007 - Europe, pages 1-6.
  6. Chang, Y.-J. and Chen, T. (2011). Multi-view 3D reconstruction for scenes under the refractive plane with known vertical direction. In ICCV'11, pages 351 - 358.
  7. Chari, V. and Sturm, P. (2009). Multiple-view geometry of the refractive plane. In Proceedings of the 20th British Machine Vision Conference, London, UK.
  8. Eustice, R. M., Pizarro, O., and Singh, H. (2008). Visually augmented navigation for autonomous underwater vehicles. IEEE Journal of Oceanic Engineering, 33(2):103-122.
  9. Ferreira, R., Costeira, J. P., and Santos, J. A. (2005). Stereo reconstruction of a submerged scene. In Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I, IbPRIA'05, pages 102-109.
  10. Gedge, J., Gong, M., and Yang, Y. (2011). Refractive epipolar geometry for underwater stereo matching. In 2011 Canadian Conference on Computer and Robot Vision (CRV), pages 146-152.
  11. Gracias, N. and Santos-Victor, J. (2000). Underwater video mosaics as visual navigation maps. Computer Vision and Image Understanding, 79:66 -91.
  12. Ishibashi, S. (2011). The study of the underwater camera model. In OCEANS, 2011 IEEE - Spain, pages 1-6.
  13. Johnson-Roberson, M., Pizarro, O., Williams, S. B., and Mahon, I. (2010). Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys. Journal of Field Robotics, 27(1):21-51.
  14. Jordt-Sedlazeck, A. and Koch, R. (2012). Refractive calibration of underwater cameras. In Proceedings of the 12th European conference on Computer Vision - Volume Part V, ECCV'12, pages 846-859.
  15. Ke, X., Sutton, M. A., Lessner, S. M., and Yost, M. (2008). Robust stereo vision and calibration methodology for accurate three-dimensional digital image correlation measurements on submerged objects. Journal of Strain Analysis for Engineering Design, 43(8):689- 704.
  16. Kunz, C. and Singh, H. (2008). Hemispherical refraction and camera calibration in underwater vision. In OCEANS 2008, pages 1-7.
  17. Kunz, C. and Singh, H. (2010). Stereo self-calibration for seafloor mapping using AUVs. In Autonomous Underwater Vehicles (AUV), 2010 IEEE/OES, pages 1-7.
  18. Kwon, Y.-H. and Casebolt, J. B. (2006). Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis. Sports Biomechanics / International Society of Biomechanics in Sports, 5(2):315-340.
  19. Lavest, J. M., Rives, G., and Lapreste, J. T. (2003). Dry camera calibration for underwater applications. Mach. Vision Appl., 13(5-6):245 - 253.
  20. Li, R., Li, H., Zou, W., Smith, R. G., and Curran, T. A. (1997). Quantitative photogrammetric analysis of digital underwater video imagery. IEEE Journal of Oceanic Engineering, 22(2):364-375.
  21. Maas, H.-G. (1995). New developments in multimedia photogrammetry. Symposium A Quarterly Journal In Modern Foreign Literatures, 8(3):150 - 5.
  22. McKinnon, D., He, H., Upcroft, B., and Smith, R. N. (2011). Towards automated and in-situ, near-real time 3-d reconstruction of coral reef environments. In OCEANS'11 MTS/IEEE Kona Conference.
  23. Meline, A., Triboulet, J., and Jouvencel, B. (2010). A camcorder for 3D underwater reconstruction of archeological objects. In OCEANS 2010, pages 1-9.
  24. Pessel, N., Opderbecke, J., and Aldon, M. J. (2003). An experimental study of a robust self-calibration method for a single camera. In Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003, volume 1, pages 522- 527.
  25. Pizarro, O., Eustice, R. M., and Singh, H. (2009). Large area 3-D reconstructions from underwater optical surveys. IEEE Journal of Oceanic Engineering, 34(2):150-169.
  26. Sedlazeck, A. and Koch, R. (2011). Calibration of housing parameters for underwater Stereo-Camera rigs. In Proceedings of the British Machine Vision Conference, pages 118.1 - 118.11.
  27. Sedlazeck, A. and Koch, R. (2012). Perspective and nonperspective camera models in underwater imaging - overview and error analysis. In Outdoor and LargeScale Real-World Scene Analysis, volume 7474 of Lecture Notes in Computer Science. Springer Berlin Heidelberg.
  28. Sedlazeck, A., Koser, K., and Koch, R. (2009). 3D reconstruction based on underwater video from ROV kiel 6000 considering underwater imaging conditions. In OCEANS 2009 - EUROPE, pages 1-10.
  29. Shortis, M., Harvey, E., and Abdo, D. (2009). A review of underwater stereo-image measurement for marine biology and ecology applications. In Oceanography and Marine Biology, volume 20092725, pages 257- 292. CRC Press.
  30. Shortis, M. R. and Harvey, E. S. (1998). Design and calibration of an underwater stereo-video system for the monitoring of marine fauna populations. International Archives Photogrammetry and Remote Sensing, 32(5):792-799.
  31. Silvatti, A. P., Salve Dias, F. A., Cerveri, P., and Barros, R. M. (2012). Comparison of different camera calibration approaches for underwater applications. Journal of Biomechanics, 45(6):1112-1116.
  32. Telem, G. and Filin, S. (2010). Photogrammetric modeling of underwater environments. ISPRS Journal of Photogrammetry and Remote Sensing, 65(5):433-444.
  33. Treibitz, T., Schechner, Y., Kunz, C., and Singh, H. (2012). Flat refractive geometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34:51-65.
  34. Yamashita, A., Kato, S., and Kaneko, T. (2006). Robust sensing against bubble noises in aquatic environments with a stereo vision system. In Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pages 928-933.
Download


Paper Citation


in Harvard Style

Dolereit T. and Kuijper A. (2014). Converting Underwater Imaging into Imaging in Air . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 96-103. DOI: 10.5220/0004685600960103


in Bibtex Style

@conference{visapp14,
author={Tim Dolereit and Arjan Kuijper},
title={Converting Underwater Imaging into Imaging in Air},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={96-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004685600960103},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Converting Underwater Imaging into Imaging in Air
SN - 978-989-758-003-1
AU - Dolereit T.
AU - Kuijper A.
PY - 2014
SP - 96
EP - 103
DO - 10.5220/0004685600960103