A probabilistic method for point matching in the presence of noise and degeneracy

Research output: Contribution to journalArticlepeer-review


The Bayesian method is widely used in image processing and computer vision to solve ill-posed problems. This is commonly achieved by introducing a prior which, together with the data constraints, determines a unique and hopefully stable solution. Choosing a "correct" prior is however a well-known obstacle. This paper demonstrates that in a certain class of motion estimation problems, the Bayesian technique of integrating out the "nuisance parameters" yields stable solutions even if a flat prior on the motion parameters is used. The advantage of the suggested method is more noticeable when the domain points approach a degenerate configuration, and/or when the noise is relatively large with respect to the size of the point configuration.

Original languageEnglish
Pages (from-to)338-346
Number of pages9
JournalJournal of Mathematical Imaging and Vision
Issue number3
StatePublished - Mar 2009


  • Bayesian analysis
  • Motion estimation
  • Nuisance parameters

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Condensed Matter Physics
  • Computer Vision and Pattern Recognition
  • Geometry and Topology
  • Applied Mathematics


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