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Non-rigid registration is central to many problems in computer vision and medical image analysis. We propose a registration algorithm which is regularized by prior knowledge in the form of a statistical deformation model. This model is obtained from previous registrations performed on a set of noise-free training examples given by images, or shapes represented by level set functions. Contrary to similar...
We show how to extend the ICP framework to nonrigid registration, while retaining the convergence properties of the original algorithm. The resulting optimal step nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable stiffness parameter. The registration loops over a series of decreasing stiffness weights, and incrementally deforms the template towards...
The registration of 3D scans of faces is a key step for many applications, in particular for building 3D morphable models. Although a number of algorithms are already available for registering data with neutral expression, the registration of scans with arbitrary expressions is typically performed under the assumption of a known, fixed identity. We present a novel algorithm which breaks this restriction,...
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