Adaptive parametrization of multivariate B-splines for image registration

We present an adaptive parametrization scheme for dynamic mesh refinement in the application of parametric image registration. The scheme is based on a refinement measure ensuring that the control points give an efficient representation of the warp fields, in terms of minimizing the registration cost function. In the current work we introduce multivariate B-splines as a novel alternative to the widely used tensor B-splines enabling us to make efficient use of the derived measure. The multivariate B-splines of order n are Cn-1 smooth and are based on Delaunay configurations of arbitrary 2D or 3D control point sets. Efficient algorithms for finding the configurations are presented, and B-splines are through their flexibility shown to feature several advantages over the tensor B-splines. In spite of efforts to make the tensor product B-splines more flexible, the knots are still bound to reside on a regular grid. In contrast, by efficient nonconstrained placement of the knots, the multiva...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Michael Sass Hansen, Rasmus Larsen, Ben Glocker, Nassir Navab
• Keywords: Adaptive Parametrization Scheme, Computer Vision, CVPR 2008, Efficient Nonconstrained Placement, Multivariate B-splines, Tensor B-splines, Tensor Product B-splines