We address the problem of nonparametric multi-modal image matching. We propose a generic framework which relies on a global variational formulation and show its versatility through three different multi-modal registration methods: supervised registration by joint intensity learning, maximization of the mutual information and maximization of the correlation ratio. Regularization is performed by using a functional borrowed from linear elasticity theory. We also consider a geometry-driven regularization method. Experiments on synthetic images and preliminary results on the realignment of MRI datasets are presented.


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    Title :

    A variational approach to multi-modal image matching


    Contributors:


    Publication date :

    2001-01-01


    Size :

    1071231 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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