<title>Sonar picture segmentation using Markovian multigrid or multiresolution algorithms</title>

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We propose two new approaches which consider the specific sonar image segmentation problem in a statistical regularization framework, based on hierarchical Markov Random Field (MRF) modeling. Within this framework , data- driven parameters estimation is performed using a mixture distributions and the contextual parameters are estimated by using the 'qualitative box' method. Then we develop two unsupervised segmentations algorithms. The first one, based on a multigrid approach, required pyramidal structure of the label field, associated to a single observation level: the MRF energy function is re-written at each scale as a coarser MRF model. The second algorithm we proposed is based on a multiresolution approach: an observation pyramid is obtained by image projection on biorthogonal wavelets. The signal to noise ratio is thus increased and allows to given a good initialization for the regularization algorithm at each level. We also compare the robustness of these unsupervised multigrid and multiresolution approaches. Some convincing results are presented and validate these new approaches for synthetic and real sonar picture segmentation.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.