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Subspace segmentation with outliers: A grassmannian approach to the maximum consensus subspace
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
Segmenting arbitrary unions of linear subspaces is an important tool for computer vision tasks such as motion and image segmentation, SfM or object recognition. We segment subspaces by searching for the orthogonal complement of the subspace supported by the majority of the observations,i.e., the maximum consensus subspace. It is formulated as a grassmannian optimization problem: a smooth, constrained but nonconvex program is immersed into the Grassmann manifold, resulting in a low dimensional
doi:10.1109/cvpr.2008.4587466
dblp:conf/cvpr/SilvaC08
fatcat:rlqzlj7hlbasvlddgfd5wgmqwy