Search Results for author: Christoph Schnörr

Found 24 papers, 2 papers with code

Learning System Parameters from Turing Patterns

no code implementations19 Aug 2021 David Schnörr, Christoph Schnörr

The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes.

Parameter Prediction

Learning Linearized Assignment Flows for Image Labeling

no code implementations2 Aug 2021 Alexander Zeilmann, Stefania Petra, Christoph Schnörr

We introduce a novel algorithm for estimating optimal parameters of linearized assignment flows for image labeling.

Self-Assignment Flows for Unsupervised Data Labeling on Graphs

no code implementations8 Nov 2019 Matthias Zisler, Artjom Zern, Stefania Petra, Christoph Schnörr

This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given.

Combinatorial Optimization

Learning Adaptive Regularization for Image Labeling Using Geometric Assignment

no code implementations22 Oct 2019 Ruben Hühnerbein, Fabrizio Savarino, Stefania Petra, Christoph Schnörr

We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth.

Numerical Integration

Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables

1 code implementation11 Jun 2019 Nikolaos Gianniotis, Christoph Schnörr, Christian Molkenthin, Sanjay Singh Bora

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals.

Bayesian Inference Variational Inference

Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment

no code implementations24 Apr 2019 Artjom Zern, Matthias Zisler, Stefania Petra, Christoph Schnörr

Experiments demonstrate a beneficial effect in both directions: adaptivity of labels improves image labeling, and steering label evolution by spatially regularized assignments leads to proper labels, because the assignment flow for supervised labeling is exactly used without any approximation for label learning.

Sum-Product Graphical Models

1 code implementation21 Aug 2017 Mattia Desana, Christoph Schnörr

SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence.

Density Estimation

Symmetry-free SDP Relaxations for Affine Subspace Clustering

no code implementations25 Jul 2016 Francesco Silvestri, Gerhard Reinelt, Christoph Schnörr

We consider clustering problems where the goal is to determine an optimal partition of a given point set in Euclidean space in terms of a collection of affine subspaces.

Joint Recursive Monocular Filtering of Camera Motion and Disparity Map

no code implementations7 Jun 2016 Johannes Berger, Christoph Schnörr

Monocular scene reconstruction is essential for modern applications such as robotics or autonomous driving.

Autonomous Driving

A Geometric Approach to Color Image Regularization

no code implementations19 May 2016 Freddie Åström, Christoph Schnörr

Our energy is a non-convex, non-smooth higher-order vectorial total variation approach and promotes color consistent image filtering via a coupling term.

Deblurring Denoising +1

Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization

no code implementations25 Apr 2016 Mattia Desana, Christoph Schnörr

Sum-Product Networks with complex probability distribution at the leaves have been shown to be powerful tractable-inference probabilistic models.

Density Estimation

Image Labeling by Assignment

no code implementations16 Mar 2016 Freddie Åström, Stefania Petra, Bernhard Schmitzer, Christoph Schnörr

We introduce a novel geometric approach to the image labeling problem.

Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes

no code implementations15 Jul 2014 Bernhard Schmitzer, Christoph Schnörr

While the overall functional is non-convex, non-convexity is confined to a low-dimensional variable.

Semantic Segmentation

Solving QVIs for Image Restoration with Adaptive Constraint Sets

no code implementations3 Jul 2014 Frank Lenzen, Jan Lellmann, Florian Becker, Christoph Schnörr

In the present paper we prove uniqueness for a larger class of problems and in particular independent of the image size.

Image Restoration

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

no code implementations2 Apr 2014 Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization

no code implementations31 Mar 2014 Fabian Rathke, Stefan Schmidt, Christoph Schnörr

With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important.

Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation

no code implementations NeurIPS 2013 Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr

We consider energy minimization for undirected graphical models, also known as MAP-inference problem for Markov random fields.

Contour Manifolds and Optimal Transport

no code implementations9 Sep 2013 Bernhard Schmitzer, Christoph Schnörr

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.

Semantic Segmentation

Convex Variational Image Restoration with Histogram Priors

no code implementations16 Jan 2013 Paul Swoboda, Christoph Schnörr

We present a novel variational approach to image restoration (e. g., denoising, inpainting, labeling) that enables to complement established variational approaches with a histogram-based prior enforcing closeness of the solution to some given empirical measure.

Denoising Image Restoration

Cannot find the paper you are looking for? You can Submit a new open access paper.