Image registration with shape mixtures

  • Simon Moss
  • Edwin R. Hancock
Poster Session C: Compression, Hardware & Software, Image Databases, Neural Networks, Object Recognition & Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


This paper describes how mixtures of Gussins be used for multiple shape template registration . The EM algorithm is applied to the shape mixture model to compute both maximum likelihood registration parameters together with set of a posteriori matching probabilities. This architecture can be viewed as providing simultaneous registration and hypothesis verification. The different templates compete to account for data through the imposed probability normalisation. Based on a sensitivity study, our main conclusions are the method is both robust to added noise and poor initialisation.


Match Probability Posteriori Probability Maximisation Step Ambiguous Figure Expectation Step 
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 1997

Authors and Affiliations

  • Simon Moss
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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