Classification of Atomic Density Distributions Using Scale Invariant Blob Localization

  • Kai Cordes
  • Oliver Topic
  • Manuel Scherer
  • Carsten Klempt
  • Bodo Rosenhahn
  • Jörn Ostermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6753)

Abstract

We present a method to classify atomic density distributions using CCD images obtained in a quantum optics experiment. The classification is based on the scale invariant detection and precise localization of the central blob in the input image structure. The key idea is the usage of an a priori known shape of the feature in the image scale space. This approach results in higher localization accuracy and more robustness against noise compared to the most accurate state of the art blob region detectors.

The classification is done with a success rate of 90% for the experimentally captured images. The results presented here are restricted to special image structures occurring in the atom optics experiment, but the presented methodology can lead to improved results for a wide class of pattern recognition and blob localization problems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Einstein, A.: Quantentheorie des einatomigen idealen gases. In: Sitzungsberichte der Preußischen Akademie der Wissenschaften Physikalisch-mathematische Klasse, pp. 261–267 (1924)Google Scholar
  2. 2.
    Anderson, M.H., Ensher, J.R., Matthews, M.R., Wieman, C.E., Cornell, E.A.: Observation of bose-einstein condensation in a dilute atomic vapor. Science 269, 198–201 (1995)CrossRefGoogle Scholar
  3. 3.
    Davis, K.B., Mewes, M.O., Andrews, M.R., van Druten, N.J., Durfee, D.S., Kurn, D.M., Ketterle, W.: Bose-einstein condensation in a gas of sodium atoms. Physical Review Letters 75, 3969–3973 (1995)CrossRefGoogle Scholar
  4. 4.
    Heisenberg, W.: Über den anschaulichen inhalt der quantentheoretischen kinematik und mechanik. Zeitschrift für Physik 43, 172–198 (1927)CrossRefMATHGoogle Scholar
  5. 5.
    Klempt, C., Topic, O., Gebreyesus, G., Scherer, M., Henninger, T., Hyllus, P., Ertmer, W., Santos, L., Arlt, J.J.: Multiresonant spinor dynamics in a bose-einstein condensate. Physical Review Letters 103, 195302 (2009)CrossRefGoogle Scholar
  6. 6.
    Klempt, C., Topic, O., Gebreyesus, G., Scherer, M., Henninger, T., Hyllus, P., Ertmer, W., Santos, L., Arlt, J.J.: Parametric amplification of vacuum fluctuations in a spinor condensate. Physical Review Letters 104, 195303 (2010)CrossRefGoogle Scholar
  7. 7.
    Scherer, M., Lücke, B., Gebreyesus, G., Topic, O., Deuretzbacher, F., Ertmer, W., Santos, L., Arlt, J.J., Klempt, C.: Spontaneous breaking of spatial and spin symmetry in spinor condensates. Physical Review Letters 105, 135302 (2010)CrossRefGoogle Scholar
  8. 8.
    Castin, Y., Dum, R.: Bose-einstein condensates in time dependent traps. Physical Review Letters 77, 5315–5319 (1996)CrossRefGoogle Scholar
  9. 9.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, vol. 3 (2008)Google Scholar
  10. 10.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision (IJCV) 60, 63–86 (2004)CrossRefGoogle Scholar
  11. 11.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference (BMVC), vol. 1, pp. 384–393 (2002)Google Scholar
  12. 12.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision (IJCV) 65, 43–72 (2005)CrossRefGoogle Scholar
  13. 13.
    Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision (IJCV) 30, 79–116 (1998)CrossRefGoogle Scholar
  14. 14.
    Lindeberg, T., Garding, J.: Shape-adapted smoothing in estimation of 3-d shape cues from affine deformations of local 2-d brightness structure. Image and Vision Computing (IVC) 15, 415–434 (1997)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV) 60, 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Cordes, K., Müller, O., Rosenhahn, B., Ostermann, J.: Bivariate feature localization for sift assuming a gaussian feature shape. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 264–275. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kai Cordes
    • 1
  • Oliver Topic
    • 2
  • Manuel Scherer
    • 2
  • Carsten Klempt
    • 2
  • Bodo Rosenhahn
    • 1
  • Jörn Ostermann
    • 1
  1. 1.Institut für Informationsverarbeitung (TNT)Leibniz Universität HannoverGermany
  2. 2.Institut für Quantenoptik (IQO)Leibniz Universität HannoverGermany

Personalised recommendations