Robust motion estimation using chrominance information in colour image sequences

  • Julian Magarey
  • Anil Kokaram
  • Nick Kingsbury
Poster Session B: Active Vision, Motion, Shape, Stereo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


This paper describes a method for incorporating the chrominance information when estimating motion in a colour image sequence. It is based on a Maximum-Likelihood (ML) formulation of the motion estimation problem which assumes homogeneous additive Gaussian noise in each colour component, with known inter-field correlation statistics. It defines a noise-decorrelating colour space transform which provides a simple implementation of the ML formulation. Results for noisy synthesised colour sequences with known motion and noise statistics demonstrate the superiority of the exact ML formulation over straightforward, unweighted three-component estimation, most noticeably in high noise conditions.


Colour Space Motion Estimation Colour Component Error Angle Motion Field 
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

  • Julian Magarey
    • 1
  • Anil Kokaram
    • 1
  • Nick Kingsbury
    • 1
  1. 1.Signal Processing and Communications LaboratoryCambridge University Engineering DepartmentCambridgeUK

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