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ORCIDDaniel Soukup
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- affiliation: AIT Austrian Institute of Technology, Vienna
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2020 – today
- 2023
[c17]Sarah Schneider
, Doris Antensteiner, Daniel Soukup, Matthias Scheutz:
Encoding Semantic Attributes - Towards Explainable AI in Industry. PETRA 2023: 518-527- 2022
- [c16]Sarah Schneider, Doris Antensteiner, Daniel Soukup, Matthias Scheutz:
Autoencoders - A Comparative Analysis in the Realm of Anomaly Detection. CVPR Workshops 2022: 1985-1991 - [c15]Sarah Schneider, Doris Antensteiner, Daniel Soukup, Matthias Scheutz:
Analysis of the Future Potential of Autoencoders in Industrial Defect Detection. VISIGRAPP (4: VISAPP) 2022: 282-289 - 2020
- [c14]Daniel Soukup:
The Necessity and Pitfall of Augmentation in Deep Learning: Observations During a Case Study in Triplet Learning for Coin Images. ICPRAM 2020: 387-394
2010 – 2019
- 2019
- [c13]Doris Antensteiner, Svorad Stolc, Daniel Soukup:
Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN. CVPR Workshops 2019: 481-483 - [c12]Thomas Pinetz, Daniel Soukup, Thomas Pock:
On the Estimation of the Wasserstein Distance in Generative Models. GCPR 2019: 156-170 - [c11]Thomas Pinetz, Johannes Ruisz, Daniel Soukup:
Actual Impact of GAN Augmentation on CNN Classification Performance. ICPRAM 2019: 15-23 - [i1]Thomas Pinetz, Daniel Soukup, Thomas Pock:
On the estimation of the Wasserstein distance in generative models. CoRR abs/1910.00888 (2019) - 2017
- [j5]Daniel Soukup, Reinhold Huber-Mörk:
Mobile hologram verification with deep learning. IPSJ Trans. Comput. Vis. Appl. 9: 9 (2017) - [c10]Teresa Klatzer, Daniel Soukup, Erich Kobler, Kerstin Hammernik, Thomas Pock:
Trainable Regularization for Multi-frame Superresolution. GCPR 2017: 90-100 - [c9]Daniel Soukup, Reinhold Huber-Mörk:
Mobile hologram verification with deep learning. MVA 2017: 169-172 - 2015
- [c8]Daniel Soukup, Svorad Stolc, Reinhold Huber-Mörk:
On Optimal Illumination for DOVID Description Using Photometric Stereo. ACIVS 2015: 553-565 - [c7]Svorad Stolc, Daniel Soukup, Reinhold Huber-Mörk:
Invariant characterization of DOVID security features using a photometric descriptor. ICIP 2015: 3422-3426 - [c6]Daniel Soukup, Svorad Stolc, Reinhold Huber-Mörk:
Analysis of optically variable devices using a photometric light-field approach. Media Watermarking, Security, and Forensics 2015: 94090R - 2014
- [j4]Svorad Stolc, Daniel Soukup, Branislav Holländer, Reinhold Huber-Mörk:
Depth and all-in-focus imaging by a multi-line-scan light-field camera. J. Electronic Imaging 23(5): 053020 (2014) - [c5]Svorad Stolc, Reinhold Huber-Mörk, Branislav Holländer, Daniel Soukup:
Depth and all-in-focus images obtained by multi-line-scan light-field approach. Image Processing: Machine Vision Applications 2014: 902407 - [c4]Reinhold Huber-Mörk, Svorad Stolc, Daniel Soukup, Branislav Holländer:
Shape from Refocus. ISVC (2) 2014: 153-162 - [c3]Daniel Soukup, Reinhold Huber-Mörk, Svorad Stolc, Branislav Holländer:
Depth Estimation within a Multi-Line-Scan Light-Field Framework. ISVC (2) 2014: 471-481 - [c2]Daniel Soukup, Reinhold Huber-Mörk:
Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images. ISVC (1) 2014: 668-677 - 2012
- [c1]Daniel Soukup, Reinhold Huber-Mörk:
Cross-Channel Co-occurrence Matrices for Robust Characterization of Surface Disruptions in 21/2D Rail Image Analysis. ACIVS 2012: 167-177
2000 – 2009
- 2009
- [j3]Daniel Soukup, Ulrich Bodenhofer, Markus Mittendorfer-Holzer, Konrad Mayer:
Semi-automatic identification of print layers from a sequence of sample images: A case study from banknote print inspection. Image Vis. Comput. 27(8): 989-998 (2009) - [j2]Dorothea Heiss-Czedik, Reinhold Huber-Mörk, Daniel Soukup, Harald Penz, Beatriz López García:
Demosaicing algorithms for area- and line-scan cameras in print inspection. J. Vis. Commun. Image Represent. 20(6): 389-398 (2009) - 2008
- [j1]Daniel Soukup, Ivan Bajla:
Robust Object Recognition under Partial Occlusions Using NMF. Comput. Intell. Neurosci. 2008 (2008)
Coauthor Index
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