Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shapecorrespondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular, we reorganize the input population into a tree structure that incorporates global information

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... about the population of shape instances, where each node in the tree represents a shape instance and each edge connects two very similar shape instances. Using this organized tree, neighboring shape instances can be corresponded efficiently and accurately by a pair-wise method. In the experiments, we evaluate the proposed method and compare its performance to five available shape correspondence methods and show the proposed method achieves the accuracy of a global method with speed of a pair-wise method. 840 978-1-4244-3991-1/09/$25.00 ©2009 IEEE