Classification of Atomic Density Distributions Using Scale Invariant Blob Localization

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


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.


Scale Space Physical Review Letter SINC Function Scale Selection Maximally Stable Extremal Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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