A Learning Approach to 3D Object Representation for Classification

Authors:
Indriyati Atmosukarto;Linda G. Shapiro
Affiliations:
Department of Computer Science and Engineering,Seattle, University of Washington, USA;Department of Computer Science and Engineering,Seattle, University of Washington, USA
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

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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.