19 citations · 23 across the 9 of their papers we have counts for
9 papers
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks
Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock +2
Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data…
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
Christoph Angermann, Simon Göppel, Markus Haltmeier
Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods…
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
Christoph Angermann, Markus Haltmeier, Ahsan Raza Siyal
Unsupervised image transfer enables intra- and inter-modality image translation in applications where a large amount of paired training data is not abundant. To ensure a structure-…
Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
Christoph Angermann, Matthias Schwab, Markus Haltmeier +2
Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. Duri…
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs
Christoph Angermann, Adéla Moravová, Markus Haltmeier +2
Real-time estimation of actual environment depth is an essential module for various autonomous system tasks such as localization, obstacle detection and pose estimation. During the…
Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks
Christoph Angermann, Markus Haltmeier, Christian Laubichler +6
State-of-the-art methods for quantifying wear in cylinder liners of large internal combustion engines require disassembly and cutting of the liner. This is followed by laboratory-b…
Deep Structure Learning using Feature Extraction in Trained Projection Space
Christoph Angermann, Markus Haltmeier
Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data. Whil…
Random 2.5D U-net for Fully 3D Segmentation
Christoph Angermann, Markus Haltmeier
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge…
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
Christoph Angermann, Markus Haltmeier, Ruth Steiger +2
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge…