### Abstract

A discussion is given of various stochastic geometry models (random fields, sequential
object processes, polygonal field models) which can be used in intermediate and high-level image analysis. Two examples are presented of actual image analysis problems (motion tracking in video, foreground/background separation) to which these ideas can be applied.

Original language | English |
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Title of host publication | New perspectives in stochastic geometry |

Editors | W.S. Kendall, I.S. Molchanov |

Place of Publication | Oxford |

Publisher | Oxford University Press |

Pages | 427-450 |

ISBN (Print) | 978-0-19-923257-4 |

Publication status | Published - 2009 |

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## Cite this

Lieshout, van, M. N. M. (2009). Applications of stochastic geometry in image analysis. In W. S. Kendall, & I. S. Molchanov (Eds.),

*New perspectives in stochastic geometry*(pp. 427-450). Oxford University Press.