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Object Recognition Using Junctions

  • Bo Wang
  • Xiang Bai
  • Xinggang Wang
  • Wenyu Liu
  • Zhuowen Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

In this paper, we propose an object detection/recognition algorithm based on a new set of shape-driven features and morphological operators. Each object class is modeled by the corner points (junctions) on its contour. We design two types of shape-context like features between the corner points, which are efficient to compute and effective in capturing the underlying shape deformation. In the testing stage, we use a recently proposed junction detection algorithm [1] to detect corner points/junctions on natural images. The detection and recognition of an object are then done by matching learned shape features to those in the input image with an efficient search strategy. The proposed system is robust to a certain degree of scale change and we obtained encouraging results on the ETHZ dataset. Our algorithm also has advantages of recognizing object parts and dealing with occlusions.

Keywords

Object Recognition Object Detection Corner Point Junction Point Shape Match 
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 2010

Authors and Affiliations

  • Bo Wang
    • 1
  • Xiang Bai
    • 1
  • Xinggang Wang
    • 1
  • Wenyu Liu
    • 1
  • Zhuowen Tu
    • 2
  1. 1.Dept. of Electronics and Information EngineeringHuazhong University of Science and TechnologyChina
  2. 2.Lab of Neuro ImagingUniversity of CaliforniaLos Angeles

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