Christoph Angermann

According to our database, Christoph Angermann authored at least 11 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of five.

2023

Correction: Unsupervised single-shot depth estimation using perceptual reconstruction.

Christoph Angermann, Matthias Schwab, Markus Haltmeier, Christian Laubichler, Steinbjörn Jónsson

Mach. Vis. Appl., November, 2023

Unsupervised single-shot depth estimation using perceptual reconstruction.

Christoph Angermann, Matthias Schwab, Markus Haltmeier, Christian Laubichler, Steinbjörn Jónsson

Mach. Vis. Appl., September, 2023

Surface topography characterization using a simple optical device and artificial neural networks.

Christoph Angermann, Markus Haltmeier, Christian Laubichler, Steinbjörn Jónsson, Matthias Schwab, Adéla Moravová, Constantin Kiesling, Martin Kober, Wolfgang Fimml

Eng. Appl. Artif. Intell., 2023

Three-dimensional Bone Image Synthesis with Generative Adversarial Networks.

Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock, Markus Haltmeier, Gerald Degenhart

CoRR, 2023

Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction.

Christoph Angermann, Simon Göppel, Markus Haltmeier

CoRR, 2023

2022

Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks.

Christoph Angermann, Markus Haltmeier, Ahsan Raza Siyal

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

2021

Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines.

Christoph Angermann, Steinbjörn Jónsson, Markus Haltmeier, Adéla Moravová, Christian Laubichler, Constantin Kiesling, Martin Kober, Wolfgang Fimml

CoRR, 2021

Deep structure learning using feature extraction in trained projection space.

Christoph Angermann, Markus Haltmeier

Comput. Electr. Eng., 2021

2020

PiNet: Deep Structure Learning using Feature Extraction in Trained Projection Space.

Christoph Angermann, Markus Haltmeier

CoRR, 2020

2019

Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation.

Christoph Angermann, Markus Haltmeier, Ruth Steiger, Sergiy Pereverzyev Jr., Elke Ruth Gizewski

CoRR, 2019

Random 2.5D U-net for Fully 3D Segmentation.

Christoph Angermann, Markus Haltmeier

Proceedings of the Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting, 2019