A Spatio-temporal Extension of the SUSAN-Filter

  • Benedikt Kaiser
  • Gunther Heidemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5163)

Abstract

This paper proposes a detector for spatio-temporal interest points. Interest point detection is a common technique in computer vision to extract salient regions and represent them by a single point for further processing. But while many algorithms exist for static images, there is hardly any method to obtain interest points from image sequences for the representation of salient motion. Here we introduce SUSANinTime, an extension of the well known SUSAN algorithm from 2D to 2D+1D, where the third dimension is time. While SUSAN-2D extracts edge- and corner points, SUSANinTime detects basic events such as turning points of object trajectories. To find out the type and saliency of the detected events, we analyze the second order statistics of the spatio-temporal volume surrounding the interest points in real world image sequences.

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References

  1. 1.
    Tian, Q., Sebe, N., Lew, M.S., Loupias, E., Huang, T.S.: Image Retrieval Using Wavelet-Based Salient Points. J. of Electronic Imaging 10(4), 835–849 (2001)CrossRefGoogle Scholar
  2. 2.
    Backer, G., Mertsching, B., Bollmann, M.: Data and Model-Driven Gaze Control for an Active-Vision System. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(12), 1415–1429 (2001)CrossRefGoogle Scholar
  3. 3.
    Heidemann, G.: Focus-of-Attention from Local Color Symmetries. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(7), 817–830 (2004)CrossRefGoogle Scholar
  4. 4.
    Privitera, C.M., Stark, L.W.: Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(9), 970–982 (2000)CrossRefGoogle Scholar
  5. 5.
    Moravec, H.P.: Towards Automatic Visual Obstacle Avoidance. In: Proc. 5th Int’l Joint Conf. on Artificial Intelligence, Cambridge, Massachusetts, USA, pp. 584–587 (1977)Google Scholar
  6. 6.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. 4th Alvey Vision Conf., pp. 147–151 (1988)Google Scholar
  7. 7.
    Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)CrossRefGoogle Scholar
  8. 8.
    Smith, S., Brady, J.: SUSAN – A New Approach to Low Level Image Processing. Int’l J. of Computer Vision 23(1), 45–78 (1997)CrossRefGoogle Scholar
  9. 9.
    Zheng, Z., Wang, H., Teoh, W.: Analysis of Gray Level Corner Detection. Pattern Recognition Letters 20, 149–162 (1999)MATHCrossRefGoogle Scholar
  10. 10.
    Zitová, B., Kautsky, J., Peters, G., Flusser, J.: Robust detection of significant points in multiframe images. Pattern Recognition Letters 20(2), 199–206 (1999)MATHCrossRefGoogle Scholar
  11. 11.
    Laptev, I., Lindeberg, T.: Space-time Interest Points. In: Proc. ICCV 2003, pp. 432–439 (2003)Google Scholar
  12. 12.
    Heidemann, G., Kaiser, B., Bax, I., Bekel, H., Ritter, H.: Spatiotemporal Events and Action Sequences. Technical report, Bielefeld Univ., Neuroinformatics Group (2005)Google Scholar
  13. 13.
    Kaiser, B., Heidemann, G.: Context-Free Detection of Events. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 223–232. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Hancock, P.J.B., Baddeley, R.J., Smith, L.S.: The Principal Components of Natural Images. Network 3, 61–70 (1992)CrossRefGoogle Scholar
  15. 15.
    Tolhurst, D.J., Tadmor, Y., Chao, T.: Amplitude spectra of natural images. Ophthalmic & Physiological Optics 12(2), 229–232 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Benedikt Kaiser
    • 1
  • Gunther Heidemann
    • 2
  1. 1.Institute for Process Control and RoboticsUniversity of KarlsruheKarlsruheGermany
  2. 2.Intelligent Systems GroupUniversity of StuttgartStuttgartGermany

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