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Object Recognition and Pose Estimation Based on Principle of Homology-Continuity

  • Zhonghua Hao
  • Shiwei Ma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Based on manifold ways of perception, this paper describes a novel method of object recognition and pose estimation within one integrated work. This method was inspired by bionic pattern recognition and manifold learning. Based on the principle of homology-continuity, we establish shortest neighborhood graph (SNG) for each class and regard it as a covering and triangulation for the hypersurface that the training data distributed on. For object recognition task, we propose a simple but effective classification method, named SNG-KNN. For pose estimation, local linear approximation method is adopted to build a local map between high-dimensional image space and low-dimensional manifold. The projective coordinates on manifold can depict the pose of object. Experiment results suggest that the recognition performance of our approach was similar and sometimes better compared to the SVM method; moreover, the pose of object can be estimated.

Keywords

Object recognition Pose estimation Shortest neighborhood graph Local linear estimation Simplex 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Mechatronic Engineering and AutomationShanghai Key Laboratory of Power Station Automation Technology, Shanghai UniversityShanghaiChina

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