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The Visual Computer

, Volume 29, Issue 4, pp 253–263 | Cite as

Cylindrical panoramic mosaicing from a pipeline video through MRF based optimization

  • Chuan Niu
  • Fan Zhong
  • Songhua Xu
  • Chenglei Yang
  • Xueying Qin
Original Article

Abstract

Stratum structure detection is a fundamental problem in geological engineering. One of the most commonly employed detection technologies is to shoot videos of a borehole using a forward moving camera. Using this technology, the problem of stratum structure detection is transformed into the problem of constructing a panoramic image from a low quality video. In this paper, we propose a novel method for creating a panoramic image of a borehole from a video sequence without the need of camera calibration and tracking. To stitch together pixels of neighboring image frames, our camera model is designed with a focal length changing feature, along with a small rotational freedom in the two-dimensional image space. Our camera model assumes that target objects lie on a cylindrical wall and that the camera moves forward along the central axis of the cylindrical wall. Based on these two assumptions, our method robustly resolves these two degrees-of-freedoms in our camera model through KLT feature tracking. Since the quality of the result video is affected by possible illumination overflow, camera lens blurring, and low video resolution, we introduce a cost function for eliminating seams between stitching strips. Our cost function is designed based on Markov Random Field and optimized using a belief propagation algorithm. Using our method, we can automatically construct a panorama image with good resolution, smoothness, and continuousness both in the texture and illumination space. Experiment results show that our method could efficiently generate panoramas for long video sequences with satisfying visual quality.

Keywords

Panorama MRF optimization Forward moving camera Belief propagation algorithm 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. U1035004, 61003149, 61272243, 61173070), Shandong Province Natural Science Foundation (Nos. ZR2010FQ011, ZR2012FQ026), and the Natural Science Fund for Distinguished Young Scholars of Shandong Province (No. JQ200920). Songhua Xu performed this research as a Eugene P. Wigner Fellow and staff member at the Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under Contract DE-AC05-00OR22725.

References

  1. 1.
    Agarwala, A., Agrawala, M., Cohen, M., Salesin, D., Szeliski, R.: Photographing long scenes with multi-viewpoint panoramas. In: ACM Trans. on Graph, pp. 853–861 (2006) Google Scholar
  2. 2.
    Behrens, A.: Creating panoramic images for bladder fluorescence endoscopy. Acta Polytech. J. Adv. Eng. 48(3), 50–54 (2008) MathSciNetGoogle Scholar
  3. 3.
    Brown, M., Lowe, G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74, 59–73 (2007) CrossRefGoogle Scholar
  4. 4.
    Chen, S.E.: Quicktime vr: An image-based approach to virtual environment navigation. In: Proc. ACM SIGGGRAPH, vol. 95, pp. 29–38 (1995) Google Scholar
  5. 5.
    Devernay, F., Faugeras, O.: Straight lines have to be straight. Mach. Vis. Appl. 13(1), 14–24 (2001) CrossRefGoogle Scholar
  6. 6.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Int. J. Comput. Vis. 70, 41–54 (2006) CrossRefGoogle Scholar
  7. 7.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984) zbMATHCrossRefGoogle Scholar
  8. 8.
    Hansen, M., Anandan, P., Dana, K., van der Wal, G., Burt, P.: Real-time scene stabilization and mosaic construction. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 54–62 (1994) Google Scholar
  9. 9.
    Harris, C., Stephens, M.J.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988) Google Scholar
  10. 10.
    Irani, M., Anandan, P., Hsu, S.: Mosaic based representations of video sequences and their applications. In: Proc. Fifth Int’l Conf Computer Vision, pp. 605–611 (1995) Google Scholar
  11. 11.
    Jaillon, P., Montanvert, A.: Image mosaicking applied to three-dimensional surfaces. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 253–257 (1994) CrossRefGoogle Scholar
  12. 12.
    Levin, G.: An informal catalogue of slit-scan video art-works (2005). http://www.flong.com/writings/lists/list_slit_scan.html
  13. 13.
    Lin, S.S., Bajcsy, R.: Catadioptric cone mirror omnidirectional imaging theory and analysis, pp. 2997–3015 (2006) Google Scholar
  14. 14.
    Lowe, G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004) CrossRefGoogle Scholar
  15. 15.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981) Google Scholar
  16. 16.
    Mann, S., Picard, R.: Virtual bellows: Constructing high quality stills from video. In: Proc. IEEE International Conference on Image Processing, vol. 1, pp. 363–367 (1994) Google Scholar
  17. 17.
    McMillan, L., Bishop, G.: Plenoptic modeling: An image-based rendering system. Proc. ACM SIGGRAPH 95, 39–46 (1995) Google Scholar
  18. 18.
    Niu, C., Zhong, F., Xu, S., Yang, C., Qin, X.: Creating cylindrical panoramic mosaic from a pipeline video. In: The 12th International Conference on CAD/Graphics, pp. 171–175 (2011) Google Scholar
  19. 19.
    Peleg, S., Rousso, B., Rav-Acha, A., Zomet, A.: Mosaicing on adaptive manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1144–1154 (2000) CrossRefGoogle Scholar
  20. 20.
    Rafajlowicz, E.: Susan edge detector reinterpreted, simplified and modified. In: International Workshop on Multidimensional (nD) Systems, pp. 9–74 (2007) Google Scholar
  21. 21.
    Gupta, R., Richard, I.H.: Linear pushbroom cameras. IEEE Trans. Pattern Anal. Mach. Intell. 19(9), 963–975 (1997) CrossRefGoogle Scholar
  22. 22.
    Rousso, B., Peleg, S., Finci, I.: Mosaicing with generalized strips. In: DARPA Image Understanding Workshop, pp. 255–260 (1997) Google Scholar
  23. 23.
    Rousso, B., Peleg, S., Finci, I., Rav-Acha, A.: Universal mosaicing using pipe projection. In: ICCV, pp. 945–952 (1998) Google Scholar
  24. 24.
    Sawhney, H.S., Ayer, S., Gorkani, M.: Model-based 2d and 3d dominant motion estimation for mosaicing and video representation. In: Proc. Fifth Int’l Conf. Computer Vision, pp. 583–590 (1995) Google Scholar
  25. 25.
    Swaminathan, R., Nayar, S.: Non-metric calibration of wide-angle lenses and polycameras. In: CVPR, vol. 2, p. 2413 (1999) Google Scholar
  26. 26.
    Szeliski, R.: Image mosaicing for tele-reality applications. In: Technical Report 94/2, Digital Equipment Corporation (1994) Google Scholar
  27. 27.
    Szeliski, R.: Image mosaicing for tele-reality applications. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 44–53 (1994) Google Scholar
  28. 28.
    Szeliski, R.: Video mosaics for virtual environments. IEEE Comput. Graph. Appl. 16, 22–30 (1996) CrossRefGoogle Scholar
  29. 29.
    Teodosio, L., Bender, W.: Panoramic overview for navigating real-world scenes. In: ACM Multimedia, vol. 93, pp. 359–364 (1993) Google Scholar
  30. 30.
    Teodosio, L., Bender, W.: Salient video stills: Content and context preserved. In: ACM Multimedia, vol. 93, p. 3946 (1993) Google Scholar
  31. 31.
    Tomasi, C., Kanade, T.: Detection and tracking of point features. In: Carnegie Mellon University Technical Report CMU-CS-91-132 (1991) Google Scholar
  32. 32.
    Zomet, A., Feldman, D., Peleg, S., Weinshall, D.: Mosaicing new views: The crossed-slits projection. IEEE Trans. Pattern Anal. Mach. Intell. 25, 741–754 (2003) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanP.R. China
  2. 2.Shandong Provincial Key Laboratory of Software EngineeringJinanP.R. China
  3. 3.Oak Ridge National LaboratoryOak RidgeUSA

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