Advertisement

A Robust Method for Registration of Partially-Overlapped Range Images Using Genetic Algorithms

  • J. W. Branch
  • F. Prieto
  • P. Boulanger
Conference paper

Abstract

Registration is a fundamental stage in the 3–D reconstruction process. We consider the problem of Euclidean alignment of two arbitrarily-oriented, partially-overlapped surfaces represented by measured point sets contaminated by noise and outliers. Given two approximately aligned range images of a real object, it is possible to carry out the registration of those images using numerous algorithms such as ICP. Basically the task is to match two or more images taken at different times, from different sensors, or from different viewpoints. In this paper, we discuss a number of possible approaches to the registration problem and propose a new method based on the manual pre-alignment of the range images of arbitrarily-oriented surfaces followed by an automatic registration process using a novel genetic optimization algorithm in 3–D data registration. Results for real range data are presented with precision and robustness, combined with the generality of genetic algorithms. This procedure focuses on the problem of obtaining the best correspondence between points through a robust search method between partially overlapped images.

Keywords

Genetic Algorithm Differential Evolution Range Image Registration Method Registration Process 
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]
    A. Myers, “Introductory literature review surface reconstruction from three dimensional range data”. Technical report, The University of Adelaide, Department of Computer Science, 1999.Google Scholar
  2. [2]
    P. J. Besl and N.D. McKay, A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2):239-256, 1992.CrossRefGoogle Scholar
  3. [3]
    Y. Chen, Object modeling by registration of multiple range images. Image and Vision Computing, 10, 1992.Google Scholar
  4. [4]
    K. Brunnstrom, Genetic algorithms for freeform surface matching. Technical report, 1996.Google Scholar
  5. [5]
    C. Robertson and R. Fisher, Parallel evolutionary registration of range data. Computer Vision and Image Understanding, pages 39-50, 2002.Google Scholar
  6. [6]
    L. Silva, O. Bellon and K. Boyer, Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 27(5):762-776, 2005.CrossRefGoogle Scholar
  7. [7]
    S. Yamany, New genetic-based technique for matching 3–D curves and surfaces. Pattern Recognition, 32(10):1817-1820, 1999.CrossRefGoogle Scholar
  8. [8]
    M. Salomon, G. Perrin and F. Heitz, Differential evolution for medical image registration. pages 201-207, 2001.Google Scholar
  9. [9]
    C. Chow, H. Tsui and T. Lee, Surface registration using a dynamic genetic algorithm. Pattern Recognition, 37(1):105,117, 2004.MATHCrossRefGoogle Scholar
  10. [10]
    S. Rusinkiewiczs, Real-time acquisition and rendering of large 3–D models. PhD thesis, Stanford University, 2001.Google Scholar
  11. [11]
    B. Horn, Closed-form solution of orientation using unit quarternions. Journal of Optical Society of America, 4, 1987.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • J. W. Branch
    • 1
  • F. Prieto
    • 2
  • P. Boulanger
    • 3
  1. 1.Escuela de SistemasUniversidad Nacional de ColombiaSede Medellín
  2. 2.Departamento de Eléctrica Electrónica y ComputaciónUniversidad Nacional de ColombiaSede Manizales
  3. 3.Department of Computing ScienceUniversity of AlbertaCanada

Personalised recommendations