Precision Mosaicking of Multi-images Based on Conic-Epipolar Constraint

  • Meng Yi
  • Yan Chu
  • Yunyi Yan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)


In this paper a robust mosaic method based on conic-epipolar constraint is proposed. The main characteristics of the proposed method include: (1) Several new methods are presented to realize fast and accurate interest points extraction under various different scenes, including SURF based feature points detection, interest points selection based on uniform distribution. (2) the transformation parameters are estimated using the invariant of conic-epipolar constraint and the most “useful” matching points are used to register the images. Experiment results illustrate that the proposed algorithm carries out real-time image registration and is robust to large image translation, scaling and rotation.


Image mosaic Image registration Multi-images Conic-epipolar constraint 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zitova, B., Flusser, J.: Image registration methods: A survey. Image Vis. Comput. 21(11), 977–1000 (2003)CrossRefGoogle Scholar
  2. 2.
    Ali, S., Reilly, V., Shah, M.: Sneddon: Motion and appearance contexts for tracking and reacquiring targets in aerial videos. In: IEEE CVPR, pp. 1–6 (2007)Google Scholar
  3. 3.
    Parag, T., Elgammal, A., Mittal, A.: A framework for feature selection for background subtraction. In: Proc. Computer Vision and Pattern Recognition, pp. 1916–1923 (2006)Google Scholar
  4. 4.
    Shum, H.-Y., Szeliski, R.: Construction of panoramic image mosaics with global and local alignment. Int. J. Comput. Vis. 36(2), 101–130 (2000)CrossRefGoogle Scholar
  5. 5.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 15(6), 415–434 (1997)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: A Performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  7. 7.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: IEEE Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)Google Scholar
  8. 8.
    Krish, K., Heinrich, S., Snyder, W.E.: Global registration of overlapping images using accumulative image features. Pattern Recognition Letters 31(2), 112–118 (2010)CrossRefGoogle Scholar
  9. 9.
    Rothwell, C.A., Zisserman, A.: Using projective invariants for constant time library indexing in model based vision. In: BMVC 1991 (1991)Google Scholar
  10. 10.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Mundy, J.L., Heller, A.: Geometric Invariance in Computer Vision. MIT Press, Cambridge (1992)Google Scholar
  12. 12.
    Kahl, F., Heyden, A.: using conic correspondences in two images to estimate the epipolar geometry. In: Proceedings of the International Conference on Computer Vision (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Electronic and Control EngineeringChang’an UniversityXi’anChina
  2. 2.School of Aerospace Science and TechnologyXidian UniversityXi’anChina

Personalised recommendations