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A comparison of stochastic and deterministic solution methods in Bayesian estimation of 2-D motion

  • Janusz Konrad
  • Eric Dubois
Optical Flow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)

Abstract

A new stochastic motion estimation method based on the Maximum A Posteriori Probability (MAP) criterion is developed. Deterministic algorithms approximating the MAP estimation over discrete and continuous state spaces are proposed. These approximations result in known motion estimation algorithms. The theoretical superiority of the stochastic algorithms over deterministic approximations in locating the global optimum is confirmed experimentally.

Keywords

Mean Square Error Gibbs Sampler Motion Field Motion Estimation Algorithm Deterministic Approximation 
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 1990

Authors and Affiliations

  • Janusz Konrad
    • 1
  • Eric Dubois
    • 1
  1. 1.INRS-TélécommunicationsVerdunCanada

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