Histogram of oriented normal vectors for object recognition with a depth sensor

  • Authors:

  • Shuai Tang;Xiaoyu Wang;Xutao Lv;Tony X. Han;James Keller;Zhihai He;Marjorie Skubic;Shihong Lao

  • Affiliations:

  • ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;ECE Department, University of Missouri, Columbia, MO;Core Technology Center of Omron Corporation, Kyoto, Japan

  • Venue:
  • ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2012

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Abstract

We propose a feature, the Histogram of Oriented Normal Vectors (HONV), designed specifically to capture local geometric characteristics for object recognition with a depth sensor. Through our derivation, the normal vector orientation represented as an ordered pair of azimuthal angle and zenith angle can be easily computed from the gradients of the depth image. We form the HONV as a concatenation of local histograms of azimuthal angle and zenith angle. Since the HONV is inherently the local distribution of the tangent plane orientation of an object surface, we use it as a feature for object detection/classification tasks. The object detection experiments on the standard RGB-D dataset [1] and a self-collected Chair-D dataset show that the HONV significantly outperforms traditional features such as HOG on the depth image and HOG on the intensity image, with an improvement of 11.6% in average precision. For object classification, the HONV achieved 5.0% improvement over state-of-the-art approaches.