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- W1548248974 abstract "This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods based on landmarks-sliding, its advantages include: (1) It can naturally deal with shapes of complex topologies and 3D dimension; (2) It is more robust against data noise; (3) The registration performance is better in terms of the generalization error of the resultant statistical shape model. These are demonstrated on both synthetic and biomedical shapes." @default.
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- W1548248974 date "2007-10-11" @default.
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- W1548248974 title "Shape Registration by Simultaneously Optimizing Representation and Transformation" @default.
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- W1548248974 doi "https://doi.org/10.1007/978-3-540-75759-7_98" @default.
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