Global Image Registration by Fast Random Projection

  • Hayato Itoh
  • Shuang Lu
  • Tomoya Sakai
  • Atsushi Imiya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


In this paper, we develop a fast global registration method using random projection to reduce the dimensionality of images. By generating many transformed images from the reference, the nearest neighbour based image registration detects the transformation which establishes the best matching from generated transformations. To reduce computational cost of the nearest nighbour search without significant loss of accuracy, we first use random projection. To reduce computational complexity of random projection, we second use spectrum-spreading technique and circular convolution.


Reference Image Image Registration Random Projection Template Image Memeory Area 
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  1. 1.
    Healy, D.M., Rohde, G.K.: Fast global image registration using random projection. In: Proc. Biomedical Imaging: From Nano to Macro, pp. 476–479 (2007)Google Scholar
  2. 2.
    Sakai, T.: An efficient algorithm of random projection by spectrum spreading and circular convolution, Inner Report IMIT Chiba University (2009)Google Scholar
  3. 3.
    Sakai, T., Imiya, A.: Practical algorithms of spectral clustering: Toward large-scale vision-based motion analysis. In: Wang, L., Zhao, G., Cheng, L., Pietikäinen, M. (eds.) Machine Learning for Vision-Based Motion Analysis Theory and Techniques. Advances in Pattern Recognition. Springer, Heidelberg (2011)Google Scholar
  4. 4.
    Zitová, B., Flusser, J.: Image registration methods: A Survey. Image Vision and Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  5. 5.
    Modersitzki, J.: Numerical Methods for Image Registration. In: CUP (2004)Google Scholar
  6. 6.
    Vempala, S.S.: The Random Projection Method. DIMACS, vol. 65 (2004)Google Scholar
  7. 7.
    Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984)CrossRefzbMATHGoogle Scholar
  8. 8.
    Frankl, P., Maehara, H.: The Johnson-Lindenstrauss lemma and the sphericity of some graphs. Journal of Combinatorial Theory Series A 44, 355–362 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 10, 19–40 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hayato Itoh
    • 1
  • Shuang Lu
    • 1
  • Tomoya Sakai
    • 2
  • Atsushi Imiya
    • 3
  1. 1.School of Advanced Integration ScienceChiba UniversityInage-kuJapan
  2. 2.Department of Computer and Information SciencesNagasaki UniversityNagasakiJapan
  3. 3.Institute of Media and Information TechnologyChiba UniversityInage-kuJapan

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