Skip to main content
SpringerLink
Log in

On Contrast-Preserving Visualisation of Multispectral Datasets

  • Conference paper
Advances in Visual Computing (ISVC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6453))

Included in the following conference series:

Abstract

Multispectral datasets are becoming increasingly common. Consequently, effective techniques to deal with this kind of data are highly sought after. In this paper, we consider the problem of joint visualisation of multispectral datasets. Several improvements to existing methods are suggested leading to a new visualisation algorithm. The proposed algorithm also produces colour images, compared to grayscale images obtained through previous methods.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Hunt, R.: Measuring colour. Fountain Press (1998)

    Google Scholar 

  2. Chukalina, M., Nikolaev, D., Somogyi, A., Schaefer, G.: Multi-technique data treatment for multi-spectral image visualization. In: 22nd European Conference on Modeling and Simulation, pp. 234–236 (2008)

    Google Scholar 

  3. Scheunders, P.: Local mapping for multispectral image visualization. Image and Vision Computing 19, 971–978 (2001)

    Article  Google Scholar 

  4. Landgrebe, D.: On information extraction principles for hyperspectral data - a white paper (1997)

    Google Scholar 

  5. Zenzo, S.D.: A note on the gradient of multi-image. Comput. Vision Graphics Image Process. 33, 116–125 (1986)

    Article  MATH  Google Scholar 

  6. Socolinsky, D., Wolff, L.: A new visualization paradigm for multispectral imagery and data fusion. In: IEEE Conf. Comp. Vis. Pat. Rec., pp. I:319–I:324(1999)

    Google Scholar 

  7. Nikolaev, D., Karpenko, S.: Color-to-grayscale image transformation preserving the gradient structure. In: 20th European Conf. Modeling & Simul., pp. 427–430 (2006)

    Google Scholar 

  8. Nikolaev, D., Nikolayev, P.: Linear color segmentation and its implementation. Color Vision and Image Understanding 94, 115–139 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, Russia

    Valeriy Sokolov, Dmitry Nikolaev & Simon Karpenko

  2. Department of Computer Science, Loughborough University, Loughborough, U.K.

    Gerald Schaefer

Authors
  1. Valeriy Sokolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dmitry Nikolaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Karpenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerald Schaefer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, 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, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammoud

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sokolov, V., Nikolaev, D., Karpenko, S., Schaefer, G. (2010). On Contrast-Preserving Visualisation of Multispectral Datasets. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17289-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17288-5

  • Online ISBN: 978-3-642-17289-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation