Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Radial basis function interpolation: numerical and analytical developments
Radial basis function interpolation: numerical and analytical developments
3D scattered data interpolation and approximation with multilevel compactly supported RBFs
Graphical Models - Special issue on SMI 2003
Education: Barycentric coordinates computation in homogeneous coordinates
Computers and Graphics
RBF-based image restoration utilising auxiliary points
Proceedings of the 2009 Computer Graphics International Conference
Rational radial basis function interpolation with applications to antenna design
Journal of Computational and Applied Mathematics
Interactive Image Inpainting Using DCT Based Exemplar Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Proceedings of the 7th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa
Duality and intersection computation in projective space with GPU support
ASM'10 Proceedings of the 4th international conference on Applied mathematics, simulation, modelling
Radial basis functions interpolation and applications: an incremental approach
ASM'10 Proceedings of the 4th international conference on Applied mathematics, simulation, modelling
A two-level approach to implicit surface modeling with compactly supported radial basis functions
Engineering with Computers
A precision of computation in the projective space
Proceedings of the 15th WSEAS international conference on Computers
Surface reconstruction with higher-order smoothness
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
Radial Basis Functions (RBF) interpolation theory is briefly introduced at the "application level" including some basic principles and computational issues. The RBF interpolation is convenient for unordered data sets in n-dimensional space, in general. This approach is convenient especially for a higher dimension N 2 conversion to ordered data set, e.g. using tessellation, is computationally very expensive. The RBF interpolation is not separable and it is based on distance of two points. The RBF interpolation leads to a solution of a Linear System of Equations (LSE) Ax = b. There are two main groups of interpolating functions: "global" and "local". Application of "local" functions, called Compactly Supporting Functions (CSFBF), can significantly decrease computational cost as they lead to a system of linear equations with a sparse matrix. The RBF interpolation can be used also for image reconstruction, inpainting removal, for solution of Partial Differential Equations (PDE) etc.