19 citations · 23 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…