A Panoramic Image Registration Algorithm Based on SURF

  • Yanju Liang
  • Qing Li
  • Zhenzhen Lin
  • Dapeng Chen
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 128)


Previous approaches have used SIFT to establish matching panoramic images featuring with huge data and time-consuming. The paper presents one technique for registering panoramic image, which uses SURF (Speeded Up Robust Features) to detect and descript the interest points, then match the interest points by using high time-efficient k-d tree Nearest Neighbor Searching method. It also eliminates mismatched points utilizing RANSAC. Lastly, we estimated transformation matrix between images. The Experiment result shows that it performs well in real time and has excellent robustness.


Neighbor Search Interest Point Panoramic Image Homography Matrix Image Registration Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Brown, M., Lowe, D.G.: Recognising Panoramas. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, vol. 2, p. 1218. IEEE Computer Society (2003)Google Scholar
  2. 2.
    Juan, L., Oubong, G.: SURF applied in panorama image stitching. In: 2010 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 495–499 (2010)Google Scholar
  3. 3.
    Stephens, C., Harris, C.: A combined corner and edge detection. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  4. 4.
    Smith, S.M., Brady, J.M.: SUSAN-A New Approach to Low Level Image Processing. Int. J. Comput. Vision 23, 45–78 (1997)CrossRefGoogle Scholar
  5. 5.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: Proceedings of the International Conference on Computer Vision, vol. 2, p. 1150. IEEE Computer Society (1999)Google Scholar
  8. 8.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  9. 9.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Henning Eberhardt, V.K., Hanebeck, U.D.: Density Trees for Efficient Nonlinear State Estimation. In: Proceedings of the 13th International Conference on Information Fusion (Year)Google Scholar
  11. 11.
    Wuhan, Y.K.: Kd-Tree Based Multi-Dimensional Indexing in the Database Application. Techniques of Atutomation And Applications 26, 9–14 (2007)Google Scholar
  12. 12.
  13. 13.
  14. 14.
    Zhang, R.-J., Zhang, J.-Q., Yang, C.: Image registration approach based on SURF. Infrared and Laser Engineering 38, 160–165 (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Yanju Liang
    • 1
  • Qing Li
    • 1
  • Zhenzhen Lin
    • 1
  • Dapeng Chen
    • 1
  1. 1.Institute of Microelectronics of Chinese Academy of ScienceBeijingChina

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