  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.
