180 citations · 1.3k across the 95 of their papers we have counts for
14 papers · 1 filter
Design, Implementation and Analysis of a Compressed Sensing Photoacoustic Projection Imaging System
Markus Haltmeier, Matthias Ye, Karoline Felbermayer +2
Significance: Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining…
Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
Nadja Gruber, Johannes Schwab, Noémie Debroux +2
We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with se…
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…
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a rec…
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-…
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determin…
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…
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Nadja Gruber, Stephan Antholzer, Werner Jaschke +2
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of…
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…
Image Based Fashion Product Recommendation with Deep Learning
Hessel Tuinhof, Clemens Pirker, Markus Haltmeier
We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style. For that purpose, a neural network classifier is used as…
Deep Learning for Photoacoustic Tomography from Sparse Data
Stephan Antholzer, Markus Haltmeier, Johannes Schwab
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data proble…