Skip to main content
SpringerLink
Log in

Global Image Registration by Fast Random Projection

  • Conference paper
Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. Sakai, T.: An efficient algorithm of random projection by spectrum spreading and circular convolution, Inner Report IMIT Chiba University (2009)

    Google Scholar 

  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. Zitová, B., Flusser, J.: Image registration methods: A Survey. Image Vision and Computing 21, 977–1000 (2003)

    Article  Google Scholar 

  5. Modersitzki, J.: Numerical Methods for Image Registration. In: CUP (2004)

    Google Scholar 

  6. Vempala, S.S.: The Random Projection Method. DIMACS, vol. 65 (2004)

    Google Scholar 

  7. Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Advanced Integration Science, Chiba University, Yayoicho 1-33, Inage-ku, Chiba, 263-8522, Japan

    Hayato Itoh & Shuang Lu

  2. Department of Computer and Information Sciences, Nagasaki University, Bunkyo-cho, Nagasaki, Japan

    Tomoya Sakai

  3. Institute of Media and Information Technology, Chiba University, Yayoicho 1-33, Inage-ku, Chiba, 263-8522, Japan

    Atsushi Imiya

Authors
  1. Hayato Itoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomoya Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atsushi Imiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Itoh, H., Lu, S., Sakai, T., Imiya, A. (2011). Global Image Registration by Fast Random Projection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24028-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation