Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-modal Image Registration by Minimising Kullback-Leibler Distance
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixtures of local experts
Neural Computation
Computer Vision and Image Understanding
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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.