In the last ten years the tensor voting framework (TVF), proposed by Medioni at al., has proved its effectiveness in perceptual grouping of arbitrary dimensional data. In the computer vision and image processing fields, this algorithm has been applied to solve various problems like stereo-matching, 3D reconstruction, and image inpainting. The TVF technique can detect and remove a big percentage of outliers, but unfortunately it does not generate satisfactory results when the data are corrupted by additive noise.

In this paper a new direct votes computation algorithm for high dimensional spaces is described, and a parametric class of decay functions is proposed to deal with noisy data. Preliminary comparative results between the original TVF and our algorithm are shown on synthetic data.