The neighbors voting algorithm and its applications

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. In this paper we propose a new technique, inspired to the TVF, that allows to estimate the dimensionality and normal orientation of the manifolds underlying a given point set. These features are encoded in tensors that can be considered as weak classifiers, whose combination is then used as a strong classifier to solve different classification problems. To prove the effectiveness of the described algorithm, three problems are considered: clustering by dimensionality estimation, image classification by manifold learning, and image inpainting by texture learning.