GCPR 2013: Pattern Recognition pp 71-80 | Cite as

Joint Shape Classification and Labeling of 3-D Objects Using the Energy Minimization Framework

  • Alexander Zouhar
  • Dmitrij Schlesinger
  • Siegfried Fuchs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)

Abstract

We propose a combination of multiple Conditional Random Field (CRF) models with a linear classifier. The model is used for the semantic labeling of 3-D surface meshes with large variability in shape. The model employs multiple CRFs of low complexity for surface labeling each of which models the distribution of labelings for a group of surfaces with a similar shape. Given a test surface the classifier exploits the MAP energies of the inferred CRF labelings to determine the shape class. We discuss the associated recognition and learning tasks and demonstrate the capability of the joint shape classification and labeling model on the object category of human outer ears.

Keywords

Ground Truth Object Category Conditional Random Field Subgradient Method Shape Context 
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 2013

Authors and Affiliations

  • Alexander Zouhar
    • 1
  • Dmitrij Schlesinger
    • 1
  • Siegfried Fuchs
    • 1
  1. 1.Dresden University of TechnologyGermany

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