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This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self‐assignment flow takes a pairwise data affinity matrix as input data and maximizes the correlation with a low‐rank matrix that is parametrized by the variables of the assignment flow, which entails an assignment of the data to themselves...
We introduce a smooth coupled system of Riemannian flows for simultaneously learning a dictionary of manifold‐valued prototypes and assigning these prototypes to the image data in a spatially coherent way. In particular, we detail a numerical update scheme for the evolution of prototypes on the Riemannian manifold of symmetric positive definite matrices. The scheme is applied to covariance descriptors...
We study an energy formulation for non-binary discrete tomography and introduce a non-convex coupling term in order to combine discrete constraints with a continuous reconstruction method based on total variation regularization. The optimization is carried out by a generalized forward–backward splitting algorithm for non-convex functions, which exploits the problem structure and is guaranteed to globally...
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