Stochastic geometry models in high-level vision

We survey the use of Markov models from stochastic geometry as priors in ‘high-level’ computer vision, in direct analogy with the use of discrete Markov random fields in ‘low-level’ vision. There are analogues of the Gibbs sampler, ICM and simulated annealing, and connections with existing methods in computer vision.