Unsupervised Markovian segmentation of sonar images
This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the an iterative method 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 field (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. (1987) 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 successfully applied to real sonar images and is compatible with an automatic treatment of massive amounts of data.