Modelling Distributed Shape Priors by Gibbs Random Fields of Second   Order
  We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express simple shapes and spatial relations between them simultaneously. This allows to model and recognise complex shapes as spatial compositions of simpler parts. 
Boris Flach and Dmitrij Schlesinger
cs.CV cs.LG