Adaptive Parametrization of Multivariate B-splines for Image Registration

Michael Sass Hansen, Benjamin Glocker, Nassir Navab, Rasmus Larsen

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    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.
    Original languageEnglish
    Title of host publicationProceedings of CVPR
    Publication date2008
    ISBN (Print)978-1-4244-2242-5
    Publication statusPublished - 2008
    Event2008 IEEE Conference on Computer Vision and Pattern Recognition - Anchorage, United States
    Duration: 23 Jun 200828 Jun 2008


    Conference2008 IEEE Conference on Computer Vision and Pattern Recognition
    Country/TerritoryUnited States
    Internet address

    Bibliographical note

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