A Probabilistic Framework for Correspondence and Egomotion

  • Justin Domke
  • Yiannis Aloimonos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4358)


This paper is an argument for two assertions: First, that by representing correspondence probabilistically, drastically more correspondence information can be extracted from images. Second, that by increasing the amount of correspondence information used, more accurate egomotion estimation is possible. We present a novel approach illustrating these principles.

We first present a framework for using Gabor filters to generate such correspondence probability distributions. Essentially, different filters ’vote’ on the correct correspondence in a way giving their relative likelihoods. Next, we use the epipolar constraint to generate a probability distribution over the possible motions. As the amount of correspondence information is increased, the set of motions yielding significant probabilities is shown to ’shrink’ to the correct motion.


Feature Point Probabilistic Framework Rotational Error Epipolar Line Independent Motion 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Justin Domke
    • 1
  • Yiannis Aloimonos
    • 1
  1. 1.Computer Vision Laboratory, Department of Computer Science, University of Maryland, College Park, MD 20742USA

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