  Statistical shape models are a useful tool in image processing and computer
vision. A Procrustres registration of the contours of the same shape is
typically perform to align the training samples to learn the statistical shape
model. A Procrustes registration between two contours with known
correspondences is straightforward. However, these correspondences are not
generally available. Manually placed landmarks are often used for
correspondence in the design of statistical shape models. However, determining
manual landmarks on the contours is time-consuming and often error-prone. One
solution to simultaneously find correspondence and registration is the
Iterative Closest Point (ICP) algorithm. However, ICP requires an initial
position of the contours that is close to registration, and it is not robust
against outliers. We propose a new strategy, based on Dynamic Time Warping,
that efficiently solves the Procrustes registration problem without
correspondences. We study the registration performance in a collection of
different shape data sets and show that our technique outperforms competing
techniques based on the ICP approach. Our strategy is applied to an ensemble of
contours of the same shape as an extension of the generalized Procrustes
analysis accounting for a lack of correspondence.
