Skip to main content
SpringerLink
Log in

Fast Image Mosaicking Using Incremental Bags of Binary Words

  • Chapter
  • First Online:
Methods for Appearance-based Loop Closure Detection

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 122))

  • 555 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://github.com/emiliofidalgo/bimos.

  2. 2.

    http://ceres-solver.org/.

References

  1. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  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. 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. Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends® Comput. Graph. Vis. 2(1), 1–104 (2006)

    Google Scholar 

  5. Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Google Scholar 

  6. Prados, R., Garcia, R., Neumann, L.: Image Blending Techniques and their Application in Underwater Mosaicing. Springer, Berlin (2014)

    Google Scholar 

  7. Elibol, A., Gracias, N., Garcia, R.: Efficient Topology Estimation for Large Scale Optical Mapping, vol. 82. Springer, Berlin (2012)

    Google Scholar 

  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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Pizarro, O., Singh, H.: Toward large-area mosaicing for underwater scientific applications. J. Ocean. Eng. 28(4), 651–672 (2003)

    Article  Google Scholar 

  19. Madjidi, H., Negahdaripour, S.: Global alignment of sensor positions with noisy motion measurements. IEEE Trans. Robot. 21(6), 1092–1104 (2005)

    Article  Google Scholar 

  20. Elibol, A., Garcia, R., Gracias, N.: A new global alignment approach for underwater optical mapping. Ocean Eng. 38(10), 1207–1219 (2011)

    Article  Google Scholar 

  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. Kekec, T., Yildirim, A., Unel, M.: A new approach to real-time mosaicing of aerial images. Rob. Auton. Syst. 62(12), 1755–1767 (2014)

    Article  Google Scholar 

  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. Botterill, T., Mills, S., Green, R.: Real-time aerial image mosaicing. In: IVCNZ, pp. 1–8 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Burt, P.J., Adelson, E.H.: A multiresolution spline with application to image mosaics. ACM Trans. Graph. 2(4), 217–236 (1983)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain

    Emilio Garcia-Fidalgo & Alberto Ortiz

Authors
  1. Emilio Garcia-Fidalgo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Ortiz .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Garcia-Fidalgo, E., Ortiz, A. (2018). Fast Image Mosaicking Using Incremental Bags of Binary Words. In: Methods for Appearance-based Loop Closure Detection. Springer Tracts in Advanced Robotics, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-75993-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75993-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75992-0

  • Online ISBN: 978-3-319-75993-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation