Girmaw Abebe Tadesse, Oliver Bent, et al.
IEEE SPM
The paper presents a new approach to least-squares spline fitting of curves. A new approximately orthogonal basis, the Q-spline basis, for n-degree uniform spline space is developed. Using the Q-spline basis, it is shown that least squares spline fitting can be approximated via a single fixed sized inner product for each control point. Another convolution maps these Q-spline control points to the classical B-spline control points. Tight error bounds on the approximation induced errors are derived. Finally a procedure for discrete least squares spline fitting via convolution is presented along with several examples. A generalization of the result has relevance to the solution of regularized fitting problems.
Girmaw Abebe Tadesse, Oliver Bent, et al.
IEEE SPM
Silvio Savarese, Holly Rushmeier, et al.
Proceedings of the IEEE International Conference on Computer Vision
Xiaohui Shen, Gang Hua, et al.
FG 2011
R.A. Gopinath, Markus Lang, et al.
ICIP 1994