In this paper, an unsupervised registration approach based on possibility theory, called "Unsupervised Possibilisticregistration", is proposed to encounter this problem. It consists on adding an unsupervised projection step that allowsmatching possibility maps, obtained from the two images instead of the grey-level images (knowing that the thematicclasses and their number have no effect on the registration). The experiments and the comparative study using MRIimages have shown promising results. It is shown that the proposed unsupervised registration approach overcomes majorproblems of existing methods and allows temporal complexity optimization.
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