A Computational Framework for Segmentation and Grouping

490 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Silhouette and stereo fusion for 3D object modeling (2003, methodology): “In order to improve even more the robustness, we can cumulate the criterion values into a 3D grid by using a voting approach as in [17, 21].”
Spectral Curvature Clustering (SCC) (2009, methodology): “2006), Tensor Voting (Medioni et al. 2000), k-Manifolds (Souvenir and Pless 2005), Grassmann Clustering (Gruber and Theis 2006), Poisson Mixture Model (Haro et al.”
Learning compact 3D models of indoor and outdoor environments with a mobile robot (2003, background): “For example, [19] presented tensor-voting to detect surfaces in range data.”
Evolutionary morphing (2005, background): “on noisy data suggested that a consistent orientation is not strictly necessary (see [13], Appendix C), we found that in our application a consistent orientation is required to get reasonable results.”
Performance of histogram descriptors for the classification of 3D laser range data in urban environments (2012, unknown): “A measure of pointness then is defined by λ̂0, surfaceness by λ̂1 − λ̂0, and linearness by λ̂2 − λ̂1 [17], [4].”
CrackTree: Automatic crack detection from pavement images (2012, methodology): “In this section, we incorporate perceptual cues of proximity and continuity to enhance the crack curves in the pavement images by using tensor voting (Guy and Medioni, 1997; Tang et al., 1999; Medioni et al., 2000 ).”