Unsupervised Segmentation Applied on 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) [1]. 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 technique to estimate the noise model parameters, and the least squares method proposed by Derin et al. [2] to estimate the MRF prior model. Then, in order to obtain an accurate segmentation map and to speed up the convergence rate, we use a multigrid strategy exploiting the previously estimated parameters. This technique has been successfully applied to real sonar images 1, and is compatible with an automatic processing of massive amounts of data.

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