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Shape Correspondence for Mixture Gaussian Shape Modeling : A Shape Categorization Approach

Many natural structures exhibit some nonlinear type of shape variation that cannot be accurately described by statistical model that assumes the underlying distribution is Gaussian. In this paper we introduce a new shape correspondence method that organizes a population of shape instances into a set of shape categories. For each shape category a local shape-tree is found where nodes in the local tree represent shape instances with similar structure. These disjoint local shape-trees are combined to form a global shape-tree that is used to correspond the population. A mixture model is then found using the correspondence result in each shape category. Qualitative and quantitative experiments are performed that show the proposed approach better estimates structures that may have nonlinear shape variations over correspondences found by the state-of-theart Minimum Description Length method.