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Learning-based non-rigid image registration using prior joint intensity distributions with graph-cuts
2011
2011 18th IEEE International Conference on Image Processing
Non-rigid image registration is widely used in medical image analysis and processing. We recently proposed a novel learning-based similarity measure for non-rigid image registration. The novel similarity measure is constructed by using two Kullback-Leibler distances (KLD), which are based on the a priori knowledge of the joint intensity distribution of a pre-aligned image pair. In this paper, we propose a new formulation for the novel KLD based similarity measure such that it can be exploited
doi:10.1109/icip.2011.6116652
dblp:conf/icip/SoC11
fatcat:qltgxyzlkjeefo2uphiteejvsy