Accelerating Integral Histograms Using an Adaptive Approach

  • Thomas Müller
  • Claus Lenz
  • Simon Barner
  • Alois Knoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

Many approaches in computer vision require multiple retrievals of histograms for rectangular patches of an input image. In 2005 an algorithm to accelerate these retrievals was presented. The data structure utilized is called Integral Histogram, which was based on the well known Integral Image.

In this paper we propose a novel approximating method to obtain these integral histograms that outperforms the original algorithm and reduces computational cost to more than a tenth. Alongside we will show that our adaptive approach still provides reasonable accuracy – which allows dramatic performance improvements for real-time applications while still being well suited for numerous computer vision tasks.

Keywords

Computer Vision Object Recognition Tracking Early Processing Integral Histogram Adaptive Approximation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pass, G., Zabih, R.: Comparing images using joint histograms. Multimedia Systems 7(3) (1999)Google Scholar
  2. 2.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  3. 3.
    Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  4. 4.
    Schiele, B., Crowley, J.L.: Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision 36(1), 31–50 (2000)CrossRefGoogle Scholar
  5. 5.
    Chang, P., Krumm, J.: Object recognition with color cooccurrence histogram. In: IEEE CVPR (1999)Google Scholar
  6. 6.
    Hetzel, G., Leibe, B., Levi, P., Schiele, B.: 3d object recognition from range images using local feature histograms. In: IEEE CVPR, vol. 2 II, pp. 394–399 (2001)Google Scholar
  7. 7.
    Laptev, I.: Improvements of object detection using boosted histograms. In: BMVC 2006, vol. III, p. 949 (2006)Google Scholar
  8. 8.
    Schneiderman, H., Kanade, T.: A statistical model for 3d object detection applied to faces and cars. In: IEEE CVPR (2000)Google Scholar
  9. 9.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE CVPR, vol. I, pp. 798–805 (2006)Google Scholar
  10. 10.
    Porikli, F.M.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: IEEE CVPR, vol. I, pp. 829–836 (2005)Google Scholar
  11. 11.
    Viola, P., Jones, M.: Robust real-time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision (2001)Google Scholar
  12. 12.
    Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math. Soc. 35, 99–109 (1943)MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas Müller
    • 1
  • Claus Lenz
    • 1
  • Simon Barner
    • 1
  • Alois Knoll
    • 1
  1. 1.Dept. of Informatics VI, Robotics and Embedded SystemsTechnische Universität MünchenGarchingGermany

Personalised recommendations