Shape Priors using Manifold Learning Techniques

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Shape Priors using Manifold Learning Techniques
We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category of shapes as a finite dimensional manifold which we approximate using Diffusion maps, that we call the shape prior manifold. Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its Nystr¨om extension. Our contribution lies in three aspects. First, we propose a solution to the pre-image problem and define the projection of a shape onto the manifold. Based on closest neighbors for the Diffusion distance, we then describe a variational framework for manifold denoising. Finally, we introduce a shape prior term for the deformable framework through a non-linear energy term designed to attract a shape towards the manifold at given constant embedding. Results on shapes of cars...
Patrick Etyngier, Florent Ségonne, Renaud K
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICCV
Authors Patrick Etyngier, Florent Ségonne, Renaud Keriven
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