Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Inference of Integrated Surface, Curve, and Junction Descriptions From Sparse 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
First Order Augmentation to Tensor Voting for Boundary Inference and Multiscale Analysis in 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emerging Topics in Computer Vision
Emerging Topics in Computer Vision
Dense Multiple View Stereo with General Camera Placement using Tensor Voting
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning (Synthesis Lectures on Image, Video, and Multimedia Processing)
Tensor Voting Fields: Direct Votes Computation and New Saliency Functions
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Motion segmentation with accurate boundaries: a tensor voting approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image Repairing: robust image synthesis by adaptive ND tensor voting
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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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.