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- Freddie Åström, Stefania Petra, Bernhard Schmitzer, Christoph Schnörr
- Journal of Mathematical Imaging and Vision
- 2016

We introduce a novel geometric approach to the image labeling problem. Abstracting from specific labeling applications, a general objective function is defined on a manifold of stochastic matrices, whose elements assign prior data that are given in any metric space, to observed image measurements. The corresponding Riemannian gradient flow entails a set of… (More)

- Bernhard Schmitzer
- SSVM
- 2015

- Bernhard Schmitzer, Christoph Schnörr
- ArXiv
- 2013

Describing shapes by suitable measures in object segmentation, as proposed in [24], allows to combine the advantages of the representations as parametrized contours and indicator functions. The pseudo-Riemannian structure of optimal transport can be used to model shapes in ways similar as with contours, while the Kantorovich functional enables the… (More)

- Bernhard Schmitzer
- Journal of Mathematical Imaging and Vision
- 2016

Discrete optimal transport solvers do not scale well on dense large problems since they do not explicitly exploit the geometric structure of the cost function. In analogy to continuous optimal transport, we provide a framework to verify global optimality of a discrete transport plan locally. This allows the construction of an algorithm to solve large dense… (More)

- Bernhard Schmitzer, Christoph Schnörr
- SSVM
- 2013

We introduce a smooth non-convex approach in a novel geometric framework which complements established convex and non-convex approaches to image labeling. The major underlying concept is a smooth manifold of probabilistic assignments of a prespecified set of prior data (the “labels”) to given image data. The Riemannian gradient flow with respect to a… (More)

This thesis focusses on the relation between causal sets and Lorentzian manifolds. Especially the effects of curvature and non-trivial topology are investigated. It is described how causal sets can be stochastically created from a given manifold via the sprinkling process. In the course of establishing a causet equivalent for the language of partial… (More)

- Bernhard Schmitzer, Christoph Schnörr
- EMMCVPR
- 2013

We gradually develop a novel functional for joint variational object segmentation and shape matching. The formulation, based on the Wasserstein distance, allows modelling of local object appearance, statistical shape variations and geometric invariance in a uniform way. For learning of class typical shape variations we adopt a recently presented approach… (More)

- Bernhard Schmitzer, Christoph Schnörr
- Journal of Mathematical Imaging and Vision
- 2012

We present a novel convex shape prior functional with potential for application in variational image segmentation. Starting point is the Gromov-Wasserstein Distance which is successfully applied in shape recognition and classification tasks but involves solving a non-convex optimization problem and which is non-convex as a function of the involved shape… (More)

- Bernhard Schmitzer
- ArXiv
- 2016

Scaling algorithms for entropic transport-type problems have become a very popular numerical method, encompassing Wasserstein barycenters, multi-marginal problems, gradient flows and unbalanced transport. However, a standard implementation of the scaling algorithm has several numerical limitations: the scaling factors diverge and convergence becomes… (More)