M. S. Hansen, B. Glocker, N. Navab, R. Larsen Adaptive Parametrization of Multivariate B-splines for Image Registration IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (USA), June 24-26, 2008 (bib)
	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 C1 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 non-constrained placement of the knots, the multivariate B-splines are shown to give a good representation of inhomogeneous objects in natural settings. The wide applicability of the method is illustrated through its application on medical data and for optical flow estimation.
	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.