GA-based parallel image registration on parallel clusters

  • Prachya Chalermwat
  • Tarek El-Ghazawi
  • Jacqueline LeMoigne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1586)


Genetic Algorithms (GAs) have been known to be robust for search and optimization problems. Image registration can take advantage of the robustness of GAs in finding best transformation between two images, of the same location with slightly different orientation, produced by moving spaceborne remote sensing instruments. In this paper, we have developed sequential and coarse-grained parallel image registration algorithms using GA as an optimization mechanism. In its first phase the algorithm finds a small set of good solutions using low-resolution versions of the images. Based on the results from the first phase, the algorithm uses full resolution image data to refine the final registration results in the second phase. Experimental results are presented and we found that our algorithms yield very accurate registration results and the parallel algorithms scales quite well on the Beowulf parallel cluster.


Genetic Algorithm Image Registration Pool Size Communication Overhead Parallel Genetic Algorithm 
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.


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  1. 1.
    J. Le Moigne. “Towards a Parallel Registration of Multiple Resolution Remote Sensing Data”, Proceedings of IGARSS’95, Firenze, Italy, July 10–14, 1995.Google Scholar
  2. 2.
    M. Corvi and G. Nicchiotti, “Multiresolution Image Registration,” in Proceedings 1995 IEEE International Conference on Image Processing, Washington, D.C., Oct. 23–26, 1995.Google Scholar
  3. 3.
    T. El-Ghazawi, P. Chalermwat, and J. LeMoigne, “Wavelet-based image Registration on parallel computers,” in SC’97: High Performance Networking and Computing: Proceedings of the 1997 ACM/IEEE SC97 Conference: November 1521, 1997.Google Scholar
  4. 4.
    J. H. Holland, “Adaptation in Natural and Artificial System,” University of Michigan Press, Ann Arbor, 1975.Google Scholar
  5. 5.
    A. Chipperfield and P. Fleming, “Parallel Genetic Algorithms,” in Parallel & Distributed Computing Handbook by A. Y. H. Zomaya, McGraw-Hill, 1996, pp. 1118–1143.Google Scholar
  6. 6.
    Mike Berry and Tarek El-Ghazawi. “Parallel Input/Output Characteristics of NASA Science Applications” Proceedings of the International Parallel Processing Symposium (IPPS’96), IEEE Computer Society Press. Honolulu, April 1996.Google Scholar
  7. 7.
    D. Ridge, D. Becker, P. Merkey, T. Sterling, “Beowulf: Harnessing the Power of Parallelism in a Pile-of-PCs,” Proceedings, IEEE Aerospace, 1997.Google Scholar
  8. 8.
    P. Husbands, “Genetic Algorithms in Optimisation and Adaptation,” in Advances in Parallel Algorithms Kronsjo and Shumsheruddin ed., 1990, pp. 227–276.Google Scholar
  9. 9.
    D. E. Goldburg, Genetic Algorithms in Search: optimization and machine learning, Reading, Mass. Addison-Wesley, 1989.Google Scholar
  10. 10.
    J. M. Fitzpatrick, J. J. Grefenstette, and D. Van-Gucht. “Image registration by genetic search,” Proceedings of Southeastcon 84, pp. 460–464, 1984.Google Scholar
  11. 11.
    M. Ozkan, J. M. Fitzpatrick, and K. Kawamura, “Image Registration for a Transputer-Based Distributed System,” in proceedings of the 2nd International Conference on Industrial & Engineering Applications of AI & Expert Systems (IEA/AIE-89), June 6–9, 1989, pp. 908–915.Google Scholar
  12. 12.
    B. Turton, T. Arslan, and D. Horrocks, “A hardware architecture for a parallel genetic algorithm for image registration,” in Proceedings of IEE Colloquium on Genetic Algorithms in Image Processing and Vision, pp. 111–116, Oct. 1994.Google Scholar
  13. 13.
    T. El-Ghazawi and J. L. Moigne, “Wavelet decomposition on high-performance computing systems,” in Proceedings of the 1996 International Conference on Parallel Processing, vol. II, pp. 19–23, May 1996.Google Scholar
  14. 14.
    P. Chalermwat, T. El-Ghazawi, and J. LeMoigne, “Image registration by parts,” in Image Registration Workshop (IRW97), pp. 299–306, NASA Goddard Space Flight Center, MD, Nov. 1997.Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Prachya Chalermwat
    • 1
  • Tarek El-Ghazawi
    • 1
  • Jacqueline LeMoigne
    • 2
  1. 1.Institute for Computational Sciences and InformaticsGeorge Mason UniversityUSA
  2. 2.NASA Goddard Space Flight CenterUSA

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