A Scatter Search Algorithm for the 3D Image Registration Problem

  • Oscar Cordón
  • Sergio Damas
  • José Santamaría
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

Image registration has been a very active research area in the computer vision community. In the last few years, there is an increasing interest on the application of Evolutionary Computation in this field and several evolutionary approaches have been proposed obtaining promising results. In this contribution we introduce the use of an advanced evolutionary algorithm, Scatter Search, to solve the 3D image registration problem. The new proposal will be validated using two different shapes (both synthetic and MRI), considering three different transformations for each of them, and testing its performance with a Basic Memetic Algorithm and the classical, problem-specific ICP algorithm.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)CrossRefGoogle Scholar
  2. 2.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Cordón, O., Damas, S., Santamaría, J.: A CHC evolutionary algorithm for 3D image registration. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 404–411. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Cordón, O., Damas, S., Bardinet, E.: 2D image registration with iterated local search. In: Benítez, J.M., Cordón, O., Hoffmann, F., Roy, R. (eds.) Advances in Soft Computing. Engineering Design and Manufacturing, pp. 233–242. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Feldmar, J., Ayache, N.: Rigid, affine and locally affine registration of free-form surfaces. International Journal of Computer Vision 18(2), 99–119 (1996)CrossRefGoogle Scholar
  6. 6.
    Glover, F.: A template for scatter search and path relinking. In: Selected Papers from the Third European Conference on Artificial Evolution, pp. 3–54 (October 1997)Google Scholar
  7. 7.
    Cotta, C., Troya, J.M.: Genetic forma recombination in permutation flowshop problems. Evolutionary Computation 6(1), 25–44 (1998)CrossRefGoogle Scholar
  8. 8.
    Han, K.P., Song, K.W., Chung, E.Y., Cho, S.J., Ha, Y.H.: Stereo matching using genetic algorithm with adaptive chromosomes. Pattern Recognition 32, 1729–1740 (2001)CrossRefGoogle Scholar
  9. 9.
    Laguna, M., Martí, R.: Scatter Search: Methodology and Implementations in C. Kluwer Academic Publishers, Boston (2003)Google Scholar
  10. 10.
    Monga, O., Benayoun, S., Faugeras, O.D.: Using partial derivatives of 3D images to extract typical surface features. In: Proc. IEEE Computer Vision and Pattern Recognition (CVPR 92), Urbana Champaign, Illinois (USA), pp. 354–359 (1992)Google Scholar
  11. 11.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial Arts: towards memetic algorithms, Technical Report, Caltech Concurrent Computation Program, C3P Report 826 (1989)Google Scholar
  12. 12.
    Yamany, S.M., Ahmed, M.N., Farag, A.A.: A new genetic-based technique for matching 3D curves and surfaces. Pattern Recognition 32, 1817–1820 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Oscar Cordón
    • 1
  • Sergio Damas
    • 2
  • José Santamaría
    • 3
  1. 1.Dept. of Computer Science and A.I.University of Granada 
  2. 2.Dept. of Software EngineeringUniversity of Granada 
  3. 3.Faculty of OdontologyUniversity of Granada 

Personalised recommendations