Hypothesis testing for coarse region estimation and stable point determination applied to Markovian texture segmentation

In this paper we show the benefits of applying hypothesis testing to the problem of texture segmentation. In our approach, hypothesis testing is used at two different stages that help to reduce the computational burden associated to iterative methods commonly used in image processing. Specifically, hypothesis testing is used to initially estimate the number of regions the image must be divided into, and to determine a set of points that will remain unchanged after the Markovian postprocessing scheme. These fixed points will contribute to reduce the number of iterations required by the Markovian stage and introduce geometry constraints that will reduce the boundary distortion caused by the stochastic procedure.