Image transport regression using mixture of experts and discrete Markov random fields

Abstract : The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.
Type de document :
Communication dans un congrès
2010 IEEE 7th International Symposium on Biomedical Imaging - ISBI 2010, Apr 2010, Rotterdam, Netherlands. pp.1229-1232, 2010, 〈10.1109/ISBI.2010.5490217〉
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Contributeur : Vivien Fécamp <>
Soumis le : vendredi 30 août 2013 - 13:43:07
Dernière modification le : mardi 5 février 2019 - 13:52:14

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Fabrice Michel, Nikos Paragios. Image transport regression using mixture of experts and discrete Markov random fields. 2010 IEEE 7th International Symposium on Biomedical Imaging - ISBI 2010, Apr 2010, Rotterdam, Netherlands. pp.1229-1232, 2010, 〈10.1109/ISBI.2010.5490217〉. 〈hal-00856054〉

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