Erez , and Bouthemy : Sonar Image Segmentation Using an Unsupervised Hierarchical Mrf Model 3

| This paper is concerned with hierarchical Markov Random Field (MRF) models and their application to sonar image segmentation. We present an original hierarchical seg-mentation procedure devoted to images given by a high resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at diierent scales, we introduce a hierarchical model involving a pyramidal label eld. It combines coarse-tone causal interactions with a spatial neighborhood structure. This new method of segmentation, called Scale Causal Multi-grid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation.