# American Institute of Mathematical Sciences

August  2011, 5(3): 551-590. doi: 10.3934/ipi.2011.5.551

## Alpha divergences based mass transport models for image matching problems

 1 Department of Mathematics, National Taiwan University, Taiwan 2 School of Mathematics, Hunan University, Changsha 410082

Received  May 2009 Revised  June 2010 Published  August 2011

Registration methods could be roughly divided into two groups: area-based methods and feature-based methods. In the literature, the Monge-Kantorovich (MK) mass transport problem has been applied to image registration as an area-based method. In this paper, we propose to use Monge-Kantorovich (MK) mass transport model as a feature-based method. This novel image matching model is a coupling of the MK problem with the well-known alpha divergence from the probability theory. The optimal matching scheme is the one which minimizes the weighted alpha divergence between two images. A primal-dual approach is employed to analyze the existence and uniqueness/non-uniqueness of the optimal matching scheme. A block coordinate method, analogous to the Sinkhorn matrix balancing method, can be used to compute the optimal matching scheme. We also derive a distance function for image morphing. Similar to elastic distances proposed by Younes, the geodesic under this distance function has an explicit expression.
Citation: Pengwen Chen, Changfeng Gui. Alpha divergences based mass transport models for image matching problems. Inverse Problems & Imaging, 2011, 5 (3) : 551-590. doi: 10.3934/ipi.2011.5.551
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