Unsupervised Markovian segmentation of sonar images

This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the recent iterative method of estimation called Iterative Conditional Estimation (ICE). This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label eld (modeled by a Markov Random Field (MRF)). For the estimation step we use a maximum likelihood estimation for the noise model parameters and the least square method proposed by Derin et al. to estimate the MRF prior model. Then, in order to obtain a good segmentation and to speed up the convergence rate, we use a multigrid strategy with the previously estimated parameters. This technique has been sucessfully applied to real sonar images and is compatible with an automatic treatment of massive amounts of data.