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 |
|---|---|
| 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.