Skip to main content
SpringerLink
Log in

Systematic Sampling in Image-Synthesis

  • Conference paper
Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3980))

Included in the following conference series:

Abstract

In this paper we investigate systematic sampling in the image- synthesis context. Systematic sampling has been widely used in stereology to improve the efficiency of different probes in experimental design. These designs are theoretically based on estimators of 1-dimensional and 2-dimensional integrals. For the particular case of the characteristic function, the variance of these estimators has been shown to be asymptotically N  − − 3/2, which improves on the O(N  − − 1) behaviour of independent estimators using uniform sampling. Thus, when no a priori knowledge of the integrand function is available, like in several image synthesis techniques, systematic sampling efficiently reduces the computational cost.

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. Baddeley, A., Vedel Jensen, E.B.: Stereology for Statisticians. Chapman & Hall/CRC (2005)

    Google Scholar 

  2. Barabesi, L.: A Monte Carlo integration approach to Horvitz-Thompson estimation in replicated environment designs. METRON – International Journal of Statistics, LXI(3), 355–374 (2003)

    Google Scholar 

  3. Cruz-Orive, L.M.: On the precision of systematic sampling: a review of Matheron’s transitive methods. Journal of Microscopy 153(Pt3), 315–333 (1989)

    Google Scholar 

  4. Cruz-Orive, L.M.: Systematic sampling in stereology. In: Proceedings 49th Session Bull. Intern. Statis. Instit., Florence, vol. 55, pp. 451–468 (1993)

    Google Scholar 

  5. Purgathofer, W.: A statistical method for adaptive stochastic sampling. Computers & Graphics 11(2), 157–162 (1987)

    Article  Google Scholar 

  6. Sobol, I.M.: Monte Carlo numerical methods. Science, Moscou (1973) (in Russian)

    Google Scholar 

  7. Keller, A., Heidrich, W.: Interleaved Sampling. In: Eurographics Workshop on Rendering 2001 (2001)

    Google Scholar 

  8. Kollig, T., Keller, A.: Efficient Multidimensional Sampling. Computer Graphics Forum 21(3), 557–563 (2002)

    Article  Google Scholar 

  9. Krishnaiah, P.R., Rao, C.R.: Handbook of statistics, vol. 6. Elsevier, Amsterdam (1988)

    Google Scholar 

  10. Cochran, W.G.: Sampling Techniques. John Wiley and sons, New York (1997)

    Google Scholar 

  11. Iones, A., Krupkin, A., Sbert, M., Zhukov, S.: Fast realistic lighting for video games. IEEE Computer Graphics & Applications (2003)

    Google Scholar 

  12. Mendez, A., Sbert, M.: Comparing Hemisphere Sampling Techniques for Obscurance Computation. In: 3IA 2004, Limoges, France (2004)

    Google Scholar 

  13. Sbert, M.: The Use of Global Random Directions to Compute Radiosity. Global Monte Carlo Techniques, PhD dissertation, Universitat Politècnica de Catalunya (1997)

    Google Scholar 

  14. Szirmay-Kalos, L.: Photorealistic Image Synthesis Using Ray-bundles, D.Sc. dissertation, Hungarian Academy of Sciences (2000)

    Google Scholar 

  15. Gual–Arnau, X., Cruz-Orive, L.M.: Systematic sampling on the circle and the sphere. Advances in Applied Probability (SGSA) 32, 628–647 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  16. Stoyan, D., Kendall, W.S., Mecke, J.: Stochastic Geometry and its Applications. Wiley, Chichester (1987)

    MATH  Google Scholar 

  17. Purgathofer, W., Tobler, R.F., Geiler, M.: Forced Random Dithering: Improved Threshold Matrices for Ordered Dithering. In: Proceedings of the First IEEE International Conference on Image Processing, Austin, Texas (1994)

    Google Scholar 

  18. Purgathofer, W., Tobler, R.F., Geiler, M.: Improved Threshold Matrices for Ordered Dithering. In: Graphics Gems V. Academic Press, London (1995)

    Google Scholar 

  19. Shirley, P.: Discrepancy as a Quality Measure for Sample Distributions. In: Proceedings of Eurographics 1991 (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut d’Informatica i Aplicacions, Universitat de Girona, Spain

    Mateu Sbert, Jaume Rigau, Miquel Feixas & Laszlo Neumann

Authors
  1. Mateu Sbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaume Rigau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miquel Feixas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laszlo Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sbert, M., Rigau, J., Feixas, M., Neumann, L. (2006). Systematic Sampling in Image-Synthesis. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_48

Download citation

  • DOI: https://doi.org/10.1007/11751540_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34070-6

  • Online ISBN: 978-3-540-34071-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation