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Statistical Foreground Modelling for Object Localisation

  • Josephine Sullivan
  • Andrew Blake
  • Jens Rittscher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)

Abstract

A Bayesian approach to object localisation is feasible given suitable likelihood models for image observations. Such a likelihood involves statistical modelling - and learning - both of the object foreground and of the scene background. Statistical background models are already quite well understood. Here we propose a “conditioned likelihood” model for the foreground, conditioned on variations both in object appearance and illumination. Its effectiveness in localising a variety of objects is demonstrated.

Keywords

Layered Sampling Conditioned Likelihood Importance Function Pattern Theory Factor Sampling 
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 2000

Authors and Affiliations

  • Josephine Sullivan
    • 1
  • Andrew Blake
    • 2
  • Jens Rittscher
    • 1
  1. 1.Dept. of EngineeringOxford UniversityOxfordUK
  2. 2.Microsoft Research LdCambridgeUK

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