Spatial dependence in the observation of visual contours

  • John MacCormick
  • Andrew Blake
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)


Two challenging problems in object recognition are: to output structures that can be interpreted statistically; and to degrade gracefully under occlusion. This paper proposes a new method for addressing both problems simultaneously. Specifically, a likelihood ratio termed the Markov discriminant is used to make statistical inferences about partially occluded objects. The Markov discriminant is based on a probabilistic model of occlusion which introduces spatial dependence between observations on the object boundary. This model is a Markov random field, which acts as the prior for Bayesian estimation of the posterior using Markov chain Monte Carlo (MCMC) simulation. The method takes as its starting point a “contour discriminant” designed to differentiate between a target and random background clutter.


Posterior Distribution Markov Chain Monte Carlo Importance Sampling Markov Random Field Measurement Line 
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 1998

Authors and Affiliations

  • John MacCormick
    • 1
  • Andrew Blake
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK

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