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We propose a method for modeling and incorporating prior shape information for Bayesian shape estimation from images. In this approach, shapes are treated as elements of an infinite-dimensional, non-linear, quotient space. Prior probability models on shape classes are defined and computed intrinsically on the tangent bundle of this shape space. MAP shape estimation is posed as a problem of gradient-based energy minimization where this energy has contributions from the image data and the prior model.