An image segmentation algorithm is described which is based on the integration of signal model parameter estimates and maximum a posteriori labelling. The parameter estimation is based on either a maximum likelihood-based method for a quadric signal model or a maximum pseudo-likelihood based method for a Gauss-Markov signal model. The first case is applicable to standard grey-level image segmentation as well as segmentation of shaded 3D surfaces, while the second case is applicable to texture segmentation. A key aspect of the algorithm is the incorporation of a coarse to fine processing strategy which limits the search for the optimum labelling at any one resolution to a subset of labellings which are consistent with the optimum labelling at the previous coarser resolution. Consistency is in terms of a prior label model which specifies the conditional probability of a given label in terms of the labelling at the previous level of resolution. It is shown how such an approach leads to a simple relaxation procedure based on local pyramid node computations. An extension of the algorithm is also described which performs accurate inter-region boundary placement using a step-wise refinement procedure based on a simple adaptive filter. The problem of automatic determination of the number of regions is also addressed. It is shown how a simple agglomerative clustering idea, again based on pyramid node computations, can effectively solve this problem.