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.
|Title of host publication||New perspectives in stochastic geometry|
|Editors||W.S. Kendall, I.S. Molchanov|
|Place of Publication||Oxford|
|Publisher||Oxford University Press|
|Publication status||Published - 2009|
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.