| 2022 | CVPR | Autoencoders - A Comparative Analysis in the Realm of Anomaly Detection. | Sarah Schneider, Doris Antensteiner, Daniel Soukup, Matthias Scheutz |
| 2020 | ICPRAM | The Necessity and Pitfall of Augmentation in Deep Learning: Observations During a Case Study in Triplet Learning for Coin Images. | Daniel Soukup |
| 2019 | CVPR | Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN. | Doris Antensteiner, Svorad Stolc, Daniel Soukup |
| 2019 | ICPRAM | Actual Impact of GAN Augmentation on CNN Classification Performance. | Thomas Pinetz, Johannes Ruisz, Daniel Soukup |
| 2017 | MVA | Mobile hologram verification with deep learning. | Daniel Soukup, Reinhold Huber-Mrk |
| 2015 | ACIVS | On Optimal Illumination for DOVID Description Using Photometric Stereo. | Daniel Soukup, Svorad Stolc, Reinhold Huber-Mrk |
| 2015 | ICIP | Invariant characterization of DOVID security features using a photometric descriptor. | Svorad Stolc, Daniel Soukup, Reinhold Huber-Mrk |
| 2014 | ISVC | Shape from Refocus. | Reinhold Huber-Mrk, Svorad Stolc, Daniel Soukup, Branislav Hollnder |
| 2014 | ISVC | Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images. | Daniel Soukup, Reinhold Huber-Mrk |
| 2014 | ISVC | Depth Estimation within a Multi-Line-Scan Light-Field Framework. | Daniel Soukup, Reinhold Huber-Mrk, Svorad Stolc, Branislav Hollnder |
| 2012 | ACIVS | Cross-Channel Co-occurrence Matrices for Robust Characterization of Surface Disruptions in 21/2D Rail Image Analysis. | Daniel Soukup, Reinhold Huber-Mrk |