no code implementations • 16 Mar 2016 • Freddie Åström, Stefania Petra, Bernhard Schmitzer, Christoph Schnörr
We introduce a novel geometric approach to the image labeling problem.
no code implementations • 24 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.
no code implementations • 22 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.
no code implementations • 8 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.
no code implementations • 12 Sep 2020 • Lukas Kiefer, Stefania Petra, Martin Storath, Andreas Weinmann
We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed tomography (CT).
no code implementations • 2 Aug 2021 • Alexander Zeilmann, Stefania Petra, Christoph Schnörr
An exact formula is derived for the parameter gradient of any loss function that is constrained by the linear system of ODEs determining the linearized assignment flow.
1 code implementation • 26 Jan 2022 • Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm.
no code implementations • 11 Jul 2022 • Sebastian Müller, Stefania Petra, Matthias Zisler
We present a geometric multilevel optimization approach that smoothly incorporates box constraints.