Fast Image Mosaicking Using Incremental Bags of Binary Words

  • Emilio Garcia-Fidalgo
  • Alberto Ortiz
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 122)


This chapter introduces a fast and multi-threaded algorithm for image mosaicking called BIMOS (Binary descriptor-based Image MOSaicking) as another example of task where appearance-based loop closure detection is of utmost importance, as it is for vision-based topological mapping. Actually, an image mosaicking process can be seen as a particular case of topological mapping given that the alignment of the images considered, which can be seen as the topology of the image sequence, has to be determined to generate the image composite. To this end, BIMOS makes use of OBIndex to find overlapping pairs. BIMOS has been validated using image sequences from several kinds of environments.


  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: European Conference on Computer Vision, Lecture Notes in Computer Science, vol. 3951, pp. 404–417 (2006)Google Scholar
  3. 3.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. IEEE Int. Conf. Comput. Vis. 95, 2564–2571 (2011)Google Scholar
  4. 4.
    Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends® Comput. Graph. Vis. 2(1), 1–104 (2006)Google Scholar
  5. 5.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)Google Scholar
  6. 6.
    Prados, R., Garcia, R., Neumann, L.: Image Blending Techniques and their Application in Underwater Mosaicing. Springer, Berlin (2014)Google Scholar
  7. 7.
    Elibol, A., Gracias, N., Garcia, R.: Efficient Topology Estimation for Large Scale Optical Mapping, vol. 82. Springer, Berlin (2012)Google Scholar
  8. 8.
    Sawhney, H.S., Hsu, S., Kumar, R.: Robust video mosaicing through topology inference and local to global alignment. In: European Conference on Computer Vision, pp. 103–119 (1998)Google Scholar
  9. 9.
    Marzotto, R., Fusiello, A., Murino, V.: High resolution video mosaicing with global alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–692–I–698 (2004)Google Scholar
  10. 10.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: European Conference on Computer Vision, Lecture Notes in Computer Science, vol. 6314, pp. 778–792 (2010)Google Scholar
  11. 11.
    Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)Google Scholar
  12. 12.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)Google Scholar
  13. 13.
    Yang, X., Cheng, K.T.: Local difference binary for ultrafast and distinctive feature description. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 188–94 (2014)CrossRefGoogle Scholar
  14. 14.
    Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision, pp. 1470–1477 (2003)Google Scholar
  15. 15.
    Garcia-Fidalgo, E., Ortiz, A., Bonnin-Pascual, F., Company, J.P.: A mosaicing approach for vessel visual inspection using a micro aerial vehicle. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2015)Google Scholar
  16. 16.
    Ridao, P., Carreras, M., Ribas, D., Garcia, R.: Visual inspection of hydroelectric dams using an autonomous underwater vehicle. J. Field Rob. 27(6), 759–778 (2010)CrossRefGoogle Scholar
  17. 17.
    Gracias, N., van der Zwaan, S., Bernardino, A., Santos-Victor, J.: Mosaic-based navigation for autonomous underwater vehicles. J. Ocean. Eng. 28(4), 609–624 (2003)CrossRefGoogle Scholar
  18. 18.
    Pizarro, O., Singh, H.: Toward large-area mosaicing for underwater scientific applications. J. Ocean. Eng. 28(4), 651–672 (2003)CrossRefGoogle Scholar
  19. 19.
    Madjidi, H., Negahdaripour, S.: Global alignment of sensor positions with noisy motion measurements. IEEE Trans. Robot. 21(6), 1092–1104 (2005)CrossRefGoogle Scholar
  20. 20.
    Elibol, A., Garcia, R., Gracias, N.: A new global alignment approach for underwater optical mapping. Ocean Eng. 38(10), 1207–1219 (2011)CrossRefGoogle Scholar
  21. 21.
    Ferreira, F., Veruggio, G., Caccia, M., Zereik, E., Bruzzone, G.: A real-time mosaicking algorithm using binary features for ROVs. In: Mediterranean Conference on Control and Automation, pp. 1267–1273 (2013)Google Scholar
  22. 22.
    Kekec, T., Yildirim, A., Unel, M.: A new approach to real-time mosaicing of aerial images. Rob. Auton. Syst. 62(12), 1755–1767 (2014)CrossRefGoogle Scholar
  23. 23.
    Bulow, H., Birk, A.: Fast and robust photomapping with an unmanned aerial vehicle (UAV). In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3368–3373 (2009)Google Scholar
  24. 24.
    Botterill, T., Mills, S., Green, R.: Real-time aerial image mosaicing. In: IVCNZ, pp. 1–8 (2010)Google Scholar
  25. 25.
    Torr, P.H., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Und. 78(1), 138–156 (2000)CrossRefGoogle Scholar
  26. 26.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: IEEE/ACM International Symposium on Mixed and Augmented Reality (ISMAR), pp. 225–234 (2007)Google Scholar
  27. 27.
    Mur-Artal, R., Tardos, J.D.: ORB-SLAM: tracking and mapping recognizable features. In: Workshop on Multi-View Geometry in Robotics (RSS) (2014)Google Scholar
  28. 28.
    Burt, P.J., Adelson, E.H.: A multiresolution spline with application to image mosaics. ACM Trans. Graph. 2(4), 217–236 (1983)CrossRefGoogle Scholar
  29. 29.
    Bonnin-Pascual, F., Ortiz, A., Garcia-Fidalgo, E., Company, J.P.: A micro-aerial platform for vessel visual inspection based on supervised autonomy. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 46–52 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of the Balearic IslandsPalma de MallorcaSpain

Personalised recommendations