Characterization of Similar Areas of Two 2D Point Clouds

  • Sébastien Mavromatis
  • Christophe Palmann
  • Jean Sequeira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)


We here present a new approach to characterize similar areas of two 2D point clouds, which is a major issue in Pattern Recognition and Image Analysis.

To do so, we define a similarity measure that takes into account several criteria such as invariance by rotation, outlier elimination, and one-dimensional structure enhancement. We use this similarity measure to associate locations from one cloud to the other, to use this result in the frame of a registration process between these two point clouds.

Our main contributions are the integration of various one-dimensional structure representations into a unified formalism, and the design of a robust estimator to evaluate the common information related to these structures.

Finally, we show how to use this approach to register images of different modalities.


Remote Sensing Point Cloud Multispectral Image Robust Estimator Similar Area 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fischler, M., Bolles, R.: Random Sample Consensus. A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Li, H., Manjunath, B., Mitra, S.: A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing 4(3), 320–334 (1995)CrossRefGoogle Scholar
  3. 3.
    Zhao, Y., Chen, Y.Q.: Connected Equi-Length Line Segments for Curve and Structure Matching. International Journal of Pattern Recognition and Artificial Intelligence 18(6), 1019–1037 (2004)CrossRefGoogle Scholar
  4. 4.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(234), 321–331 (1988)CrossRefGoogle Scholar
  5. 5.
    Touzi, R., Lopes, A., Bousquet, P.: A statistical and geometrical edge detector for SAR images. IEEE Transactions on Geoscience and Remote Sensing 30(5), 1054–1060 (1988)Google Scholar
  6. 6.
    Deriche, R.: Using Canny’s criteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision 1(2), 167–187 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sébastien Mavromatis
    • 1
  • Christophe Palmann
    • 1
  • Jean Sequeira
    • 1
  1. 1.Projet SimGraph - LSIS UMR CNRS 7296Aix-Marseille Université Polytech Marseille case 925Marseille Cedex 9France

Personalised recommendations