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Image Feature Extraction Using Gradient Local Auto-Correlations

  • Takumi Kobayashi
  • Nobuyuki Otsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)

Abstract

In this paper, we propose a method for extracting image features which utilizes 2nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT.

Keywords

Image Patch Image Gradient Mask Pattern Human Detection Torus Manifold 
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 2008

Authors and Affiliations

  • Takumi Kobayashi
    • 1
  • Nobuyuki Otsu
    • 1
  1. 1.National Institute of Advanced Industrial Science and TechnologyTsukubaJapan

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