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 non- constrained placement of the knots, the multivariate B- splines are shown to give a good representation of inho- mogeneous objects in natural settings. The wide applicability of the method is illustrated through its application on medical data and for optical flow estimation.
|Title of host publication||Proceedings of CVPR|
|Publication status||Published - 2008|
|Event||2008 IEEE Conference on Computer Vision and Pattern Recognition - Anchorage, United States|
Duration: 23 Jun 2008 → 28 Jun 2008
|Conference||2008 IEEE Conference on Computer Vision and Pattern Recognition|
|Period||23/06/2008 → 28/06/2008|