Modeling Shapes with Higher-Order Graphs: Methodology and Applications

  • Chaohui WangEmail author
  • Yun Zeng
  • Dimitris Samaras
  • Nikos Paragios
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Extrinsic factors such as object pose and camera parameters are a main source of shape variability and pose an obstacle to efficiently solving shape matching and inference. Most existing methods address the influence of extrinsic factors by decomposing the transformation of the source shape (model) into two parts: one corresponding to the extrinsic factors and the other accounting for intra-class variability and noise, which are solved in a successive or alternating manner. In this chapter, we consider a methodology to circumvent the influence of extrinsic factors by exploiting shape properties that are invariant to them. Based on higher-order graph-based models, we implement such a methodology to address various important vision problems, such as non-rigid 3D surface matching and knowledge-based 3D segmentation, in a one-shot optimization scheme. Experimental results demonstrate the superior performance and potential of this type of approach.


Extrinsic Factor Markov Random Field Iterative Close Point Shape Match Markov Random Field Model 
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 London 2013

Authors and Affiliations

  • Chaohui Wang
    • 1
    Email author
  • Yun Zeng
    • 2
  • Dimitris Samaras
    • 2
  • Nikos Paragios
    • 3
    • 4
    • 5
  1. 1.Vision LabUniversity of CaliforniaLos AngelesUSA
  2. 2.Dept. of Computer ScienceStony Brook UniversityStony BrookUSA
  3. 3.Center for Visual ComputingEcole Centrale ParisChâtenay-Malabry CedexFrance
  4. 4.LIGM LaboratoryUniversity Paris-East & Ecole des Ponts Paris-TechMarne-la-ValléeFrance
  5. 5.GALEN GroupINRIA Saclay - Île-de-FranceRocquencourtFrance

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