Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 14, pp 19437–19455 | Cite as

A novel monocular calibration method for underwater vision measurement

  • Zhe Chen
  • Ruili WangEmail author
  • Wanting Ji
  • Ming Zong
  • Tanghuai Fan
  • Huibin Wang
Article
  • 161 Downloads

Abstract

Vision measurement systems have a reliable performance on ground, but it remains a challenge to apply commonly-used vision measurement systems (i.e. multi-camera systems and laser systems) in underwater environments. One of the most challenging issues is the transformation from an unscaled measurement to a scaled result, which is achieved by a calibration method and determinate the strategy used for underwater vision measurement. This paper proposes a novel monocular underwater calibration method underlying a simple underwater vision measurement system. Underwater unscaled measurement results are calculated by the dark channel prior model. These results are then processed by our calibration method, transforming the unscaled measurements to accurately scaled results. These measurement results finally are used to estimate the scaled 3D structure of underwater objects. Experimental results under natural open water show that our proposed method is reliable and efficient.

Keywords

Vision measurement Monocular system Underwater environment Three-dimensional structure 

Notes

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 61563036, 61671201), the Fundamental Research Funds for the Central Universities (No. 2017B01914), the Marsden Fund of New Zealand.

References

  1. 1.
    Arnush D (1972) Underwater light-beam propagation in the small-angle-scattering approximation. JOSA 62(9):1109–1011CrossRefGoogle Scholar
  2. 2.
    Bosch J, Gracias N, Ridao P, Ribas D (2015) Omnidirectional underwater camera design and calibration. Sensors 15(3):6033–6065CrossRefGoogle Scholar
  3. 3.
    Chen Z (2018) Underwater Object Detection. Available online: https://github.com/9434011/underwater-vision-measurement (accessed on 22/08/2018)
  4. 4.
    Fryer JG, Fraser CS (1986) On the calibration of underwater cameras. Photogramm Rec 12(67):73–85CrossRefGoogle Scholar
  5. 5.
    Hildebrandt M, Albiez J, Kirchner F (2008) Computer-based control of deep-sea manipulators. OCEANS 2008-MTS/IEEE Kobe Techno-Ocean:1–6Google Scholar
  6. 6.
    Iqbal K, Abdul Salam R, Osman M, Talib AZ (2007) Underwater image enhancement using an integrated colour model. IAENG Int J Comput Sci 32(2):239–244Google Scholar
  7. 7.
    Jaffe JS (2015) Underwater optical imaging: the past, the present, and the prospects. IEEE J Ocean Eng 40(3):683–700CrossRefGoogle Scholar
  8. 8.
    Jia Z, Yang J, Liu W, Wang F, Liu Y, Wang L, Fan C, Zhao K (2015) Improved camera calibration method based on perpendicularity compensation for binocular stereo vision measurement system. Opt Express 23(12):15205–15223CrossRefGoogle Scholar
  9. 9.
    Jian M, Dong J, Lam KMFSAM (2013) A fast self-adaptive method for correcting non-uniform illumination for 3D reconstruction. Comput Ind 64(9):1229–1236CrossRefGoogle Scholar
  10. 10.
    Jian M, Lam KM, Dong J (2014) Illumination-insensitive texture discrimination based on illumination compensation and enhancement. Inf Sci 269:60–72MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jian M, Qi Q, Dong J, Yin Y, Lam KM (2017) The OUC-vision large-scale underwater image database. IEEE International Conference on Multimedia and Expo (ICME) 2017:1297–1302CrossRefGoogle Scholar
  12. 12.
    Jian M, Yin Y, Dong J, Zhang W (2018) Comprehensive assessment of non-uniform illumination for 3D heightmap reconstruction in outdoor environments. Comput Ind 99:110–118CrossRefGoogle Scholar
  13. 13.
    Jian M, Qi Q, Dong J, Yin Y, Lam KM (2018) Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection. J Vis Commun Image Represent 53:31–41CrossRefGoogle Scholar
  14. 14.
    Johnson-Roberson M, Pizarro O, Williams SB, Mahon I (2010) Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys. Journal of Field Robotics 27(1):21–51CrossRefGoogle Scholar
  15. 15.
    Johnson-Roberson M, Bryson M, Friedman A, Pizarro O, Troni G, Ozog P, Henderson JC (2017) High-resolution underwater robotic vision-based mapping and three-dimensional reconstruction for archaeology. Journal of Field Robotics. 34(4):625–643CrossRefGoogle Scholar
  16. 16.
    Jordt-Sedlazeck A, Koch R (2012) Refractive calibration of underwater cameras. European conference on computer vision:846–859Google Scholar
  17. 17.
    Kim SH, Chung KY (2014) 3D simulator for stability analysis of finite slope causing plane activity. Multimed Tools Appl 68(2):455–463CrossRefGoogle Scholar
  18. 18.
    Kristensson E, Berrocal E, Aldén M (2014) Two-pulse structured illumination imaging. Opt Lett 39(9):2584–2587CrossRefGoogle Scholar
  19. 19.
    Lavest JM, Rives G, Lapresté JT (2000) Underwater camera calibration. European Conference on Computer Vision:654–668Google Scholar
  20. 20.
    Lu H, Li Y, Nakashima S, Serikawa S (2016) Single image dehazing through improved atmospheric light estimation. Multimed Tools Appl 75(24):17081–17096CrossRefGoogle Scholar
  21. 21.
    Mallios A, Ridao P, Ribas D, Carreras M, Camilli R (2016) Toward autonomous exploration in confined underwater environments. Journal of Field Robotics. 33(7):994–1012CrossRefGoogle Scholar
  22. 22.
    Miura N, Asano Y (2016) Effective acquisition protocol of terrestrial laser scanning for underwater topography in a Steep Mountain channel. River Res Appl 32(7):1621–1631CrossRefGoogle Scholar
  23. 23.
    Muljowidodo K, Rasyid MA, SaptoAdi N, Budiyono A (2009) Vision based distance measurement system using single laser pointer design for underwater vehicle. Indian Journal of Geo-Marine Science 38(3):324–331Google Scholar
  24. 24.
    Neto DM, Oliveira MC, Menezes LF, Alves JL (2013) Improving Nagata patch interpolation applied for tool surface description in sheet metal forming simulation. Comput Aided Des 45(3):639–656MathSciNetCrossRefGoogle Scholar
  25. 25.
    Russell C, Yu R, Agapito L (2014) Video pop-up: monocular 3d reconstruction of dynamic scenes. European conference on computer vision:583–598Google Scholar
  26. 26.
    Sakka T, Tamura A, Matsumoto A, Fukami K, Nishi N, Thornton B (2014) Effects of pulse width on nascent laser-induced bubbles for underwater laser-induced breakdown spectroscopy. Spectrochim Acta B At Spectrosc 97:94–98CrossRefGoogle Scholar
  27. 27.
    Sanz PJ, Penalver A, Sales J, Fornas D, Fernandez JJ, Pérez J, Bernabe J. (2013) Grasper: A multisensory based manipulation system for underwater operations. In Systems, Man, and Cybernetics (SMC), IEEE International Conference on 2013, 4036–4041Google Scholar
  28. 28.
    Schechner YY, Karpel N (2004) Clear underwater vision. IEEE computer society conference on computer vision and. Pattern Recogn:1–11Google Scholar
  29. 29.
    Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Computers & Electrical Engineering 40(1):41–50CrossRefGoogle Scholar
  30. 30.
    Shortis M (2015) Calibration techniques for accurate measurements by underwater camera systems. Sensors 15(12):10–26CrossRefGoogle Scholar
  31. 31.
    Sim KS, Tso CP, Tan YY (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28(10):1209–1221CrossRefGoogle Scholar
  32. 32.
    Sipiran I, Bustos B (2011) Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. Vis Comput 27(11):963–976CrossRefGoogle Scholar
  33. 33.
    Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage LearningGoogle Scholar
  34. 34.
    Spampinato C, Palazzo S, Boom B, van Ossenbruggen J, Kavasidis I, Di Salvo R, Lin FP, Giordano D, Hardman L, Fisher RB (2014) Understanding fish behavior during typhoon events in real-life underwater environments. Multimed Tools Appl 70(1):199–236CrossRefGoogle Scholar
  35. 35.
    Sporer M, Lurz F, Schluecker E, Weigel R, Koelpin A (2015) Underwater interferometric radar sensor for distance and vibration measurement. IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet):72–74Google Scholar
  36. 36.
    Wang JB, He N, Zhang LL (2015) Lu K. single image dehazing with a physical model and dark channel prior. Neurocomputing 149:718–728CrossRefGoogle Scholar
  37. 37.
    Wang Y, Qin N, Zhao N (2015) Delaunay graph and radial basis function for fast quality mesh deformation. J Comput Phys 294:149–172MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Wang K, Gao H, Xu X, Jiang J, Yue D (2016) An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks. IEEE Sensors J 16(11):4051–4062CrossRefGoogle Scholar
  39. 39.
    Yamakita T, Sudo K, Jintsu-Uchifune Y, Yamamoto H, Shirayama Y (2017) Identification of important marine areas using ecologically or biologically significant areas (EBSAs) criteria in the east to Southeast Asia region and comparison with existing registered areas for the purpose of conservation. Mar Policy 81:273–284CrossRefGoogle Scholar
  40. 40.
    Yau T, Gong M, Yang YH (2013) Underwater camera calibration using wavelength triangulation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR):2499–2506Google Scholar
  41. 41.
    Zhu X, Zhang S, Hu R, Zhu Y, Song J (2018) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517–529CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhe Chen
    • 1
  • Ruili Wang
    • 2
    Email author
  • Wanting Ji
    • 2
  • Ming Zong
    • 2
  • Tanghuai Fan
    • 3
  • Huibin Wang
    • 1
  1. 1.College of computer and informationHohai UniversityNanjingChina
  2. 2.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand
  3. 3.School of Information EngineeringNanchang Institute of TechnologyNanchangChina

Personalised recommendations