Advertisement

Efficient Methods for Point Matching with Known Camera Orientation

  • João F. C. Mota
  • Pedro M. Q. Aguiar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6111)

Abstract

The vast majority of methods that successfully recover 3D structure from 2D images hinge on a preliminary identification of corresponding feature points. When the images capture close views, e.g., in a video sequence, corresponding points can be found by using local pattern matching methods. However, to better constrain the 3D inference problem, the views must be far apart, leading to challenging point matching problems. In the recent past, researchers have then dealt with the combinatorial explosion that arises when searching among N! possible ways of matching N points. In this paper we overcome this search by making use of prior knowledge that is available in many situations: the orientation of the camera. This knowledge enables us to derive \(\mathcal{O}(N^2)\) algorithms to compute point correspondences. We prove that our approach computes the correct solution when dealing with noiseless data and derive an heuristic that results robust to the measurement noise and the uncertainty in prior knowledge. Although we model the camera using orthography, our experiments illustrate that our method is able to deal with violations, including the perspective effects of general real images.

Keywords

Feature Point Permutation Matrix Point Match Combinatorial Explosion Orthographic Projection 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hartley, R., Zisserman, A.: Multiple View Geometry In Computer Vision. Cambridge University Press, Cambridge (2003)Google Scholar
  2. 2.
    Dellaert, F., Seitz, S., Thorpe, C., Thrun, S.: Structure from motion without correspondence. In: IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head SC, USA (2000)Google Scholar
  3. 3.
    Shivaswamy, P., Jebara, T.: Permutation invariant SVMs. In: Int. Conf. on Machine Learning, Pittsburgh PA, USA (2006)Google Scholar
  4. 4.
    Martins, A., Aguiar, P., Figueiredo, M.: Orientation in Manhattan. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(5) (2005)Google Scholar
  5. 5.
    Aguiar, P., Moura, J.: Rank 1 weighted factorization for 3D structure recovery: Algorithms and performance analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9) (2003)Google Scholar
  6. 6.
    Bouguet, J.: Camera calibration toolbox for Matlab (2008), http://www.vision.caltech.edu/bouguetj/calib_doc

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • João F. C. Mota
    • 1
  • Pedro M. Q. Aguiar
    • 1
  1. 1.Institute for Systems and Robotics / ISTLisboaPortugal

Personalised recommendations