An edge-based hierarchical algorithm for textured image segmentation

An edge-based hierarchical algorithm is proposed for segmenting textured images. The texture regions are modeled by Gaussian Markov random fields. No prior knowledge about the texture parameter values or the number of texture regions is assumed. The algorithm consists of two stages: boundary (edge) detection and edge estimation. In the first stage, the image is divided into disjoint square windows. The windows on the boundary of the regions are detected through a hypothesis test. The generalized likelihood ratio (GLR) shows some asymptotic optimality for the test. To reduce the computational cost associated with the GLR, average periodogram and low rank approximation are applied. The exact locations of the edges are hierarchically estimated in the second stage by a maximum likelihood estimator. Simulation and experimental examples are discussed