Fast Multiple Shape Correspondence by Pre-Organizing Shape Instances

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 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 sp...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Andrew Temlyakov, Brent C. Munsell, Song Wang
• Keywords: Computer Vision, CVPR 2009, Multiple Shape Correspondence