A gabor wavelet pyramid-based object detection algorithm

  • Authors:

  • Yasuomi D. Sato;Jenia Jitsev;Joerg Bornschein;Daniela Pamplona;Christian Keck;Christoph von der Malsburg

  • Affiliations:

  • Department of Brain Science and Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan and Frankfurt Institute for Advanced Studies ...;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany and Max-Planck-Institute for Neurological Research, Koeln, Germany;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2011

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Abstract

We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the correspondingly sub-sampled image smoothed with the low-pass filter. The multiscale feature correspondence is used for effectively reducing computational costs in filtering. In particular, we show that the multi-scale Gabor feature correspondence play an effective role in matching between an input image and the model representation for object detection.