Consistent estimation of a mean pattern in deformable models for high-dimensional shape analysis

Details Consistent estimation of a mean pattern in deformable models for high-dimensional shape analysis Consistent estimation of a mean pattern in deformable models for high-dimensional shape analysis

2011-10-25

arxiv.org/abs/1110.5415v1

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We consider the problem of estimating a mean shape from a set of J planar configurations described by a sequence of k landmarks. We study the consistency of a smoothed Procrustean mean when the observations obey a deformable model including some nuisance parameters such as random translations, rotations and scaling. The main contribution of the paper is to analyze the influence of the dimension k of the data and of the number J of observed configurations on the convergence of the smoothed Procrustean estimator to the mean pattern of the model. Some numerical experiments illustrate these results.

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