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Abstract

In this paper we describe our 3D object signature for 3D object classification. The signature is based on a learning approach that finds salient points on a 3D object and represent these points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show high classification rates on both pose-normalized and rotated objects and include a study on classification accuracy as a function of number of rotations in the training set.

Keywords

3D Object Classification 3D Object Signature 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Indriyati Atmosukarto
    • 1
  • Linda G. Shapiro
    • 1
  1. 1.Department of Computer Science and Engineering,SeattleUniversity of WashingtonUSA

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