Learning an Object Model for Feature Matching in Clutter

  • Toni Tamminen
  • Jouko Lampinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


We consider the problem of learning an object model for feature matching. The matching system is Bayesian in nature with separate likelihood and prior parts. The likelihood is based on Gabor filter responses, which are modelled as probability distributions in the filter response vector space. The prior model for the object shape is learnt in two stages: in the first stage we assume only the mean shape known, with independent variations for each feature point, and match ‘easy’ images. We then estimate the characteristics of the shape variations for a realistic prior on the shapes. We demonstrate how incorporating the shape variation prior into the matching model enhances matching performance in the presence of clutter.


Face Recognition Object Model Shape Variation Feature Match Face Shape 
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 2003

Authors and Affiliations

  • Toni Tamminen
    • 1
  • Jouko Lampinen
    • 1
  1. 1.Laboratory of Computational EngineeringHelsinki University of TechnologyFinland

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