ENEE739J: Markov Random Fields for Texture Segmentation

One important and natural application of discrete Markov Random Fields [4] in Image analysis is for texture modelling and segmenation [5]. In this report we describe texture segmentation algorithms which have been performed on two mosaics (See Figure 1). Markov Random Fields were used to model the textures and the textures were distinguished by their parameters, θ, μ, and σ. The Minimum Variance Estimate estimate for the (i, j) pixel xi,j is given in terms of the ordered vector formed by the neighbourhood pixels of xi,j , x s i,j and noise ei,j . We assume that xi,j is wide sense stationary and has mean μi,j = E(xi,j) = μ.