Abstract
Recently 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 field, this algorithm has been applied to solve various problems as stereo-matching, boundary inference, and image inpainting. In the last decade the TVF was augmented with new saliency features, like curvature and first order tensors. In this paper a new curvature estimation technique is described and its effectiveness, when used with the saliency functions proposed in [1], is demonstrated. Results are shown for synthetic datasets in spaces of different dimensionalities.
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