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Invariant Features for 3D-Data based on Group Integration using Directional Information and Spherical Harmonic Expansion
 

Summary: Invariant Features for 3D-Data based on Group Integration using
Directional Information and Spherical Harmonic Expansion
M. Reisert and H.Burkhardt
Computer Science Department, University of Freiburg, 79110 Freiburg i. Br., Germany
Abstract
Due to the increasing amount of 3D data for vari-
ous applications there is a growing need for classifica-
tion and search in such databases. As the representa-
tion of 3D objects is not canonical and objects often
occur at different spatial position and in different ro-
tational poses, the question arises how to compare and
classify the objects. One way is to use invariant fea-
tures. Group Integration is a constructive approach to
generate invariant features. Several variants of Group
Integration features are already proposed. In this paper
we present two main extensions, we include local di-
rectional information and use the Spherical Harmonic
Expansion to compute more descriptive features. We
apply our methods to 3D-volume data (Pollen grains)
and 3D-surface data (Princeton Shape Benchmark)

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences