Reduced Inverse Distance Weighting Interpolation for Painterly Rendering

The interpolation problem of irregularly distributed data, in a multidimensional domain is considered. A modification of the inverse distance weighting interpolation formula. is proposed; making computation time independent of the number of data, points. Only the first K neighbors of a given point are considered, instead of the entire dataset. Additional factors are introduced, preventing discontinuities on points where tire set; of local neighbors charges. Theoretical analysis provides conditions which guarantee continuity. The proposed approach is efficient and free front magic numbers. Unlike many existing algorithms based on the k-nearest neighbors the number of neighbors is derived from theoretical principles. The method has been applied to the problem of vector field generation in the context of artistic imaging. Experimental results show its ability to produce brush strokes oriented along object contours and to effectively render meaningful texture details.