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ORCIDFrederik Beuth
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- affiliation: Chemnitz University of Technology, Department of Computer Science, Germany
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Books and Theses
- 2020
[b1]Frederik Beuth:
Visual attention in primates and for machines - neuronal mechanisms. Chemnitz University of Technology, Germany, 2020, ISBN 978-3-96100-102-6
Journal Articles
- 2023
- [j4]Micha Burkhardt
, Julia Bergelt, Lorenz Gönner, Helge Ulo Dinkelbach, Frederik Beuth, Alex Schwarz, Andrej Bicanski, Neil Burgess, Fred H. Hamker:
A large-scale neurocomputational model of spatial cognition integrating memory with vision. Neural Networks 167: 473-488 (2023) - 2022
- [j3]Tobias Schlosser, Michael Friedrich, Frederik Beuth, Danny Kowerko:
Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. J. Intell. Manuf. 33(4): 1099-1123 (2022) - 2014
- [j2]Marco Antonelli, Agostino Gibaldi, Frederik Beuth, Angel Juan Duran, Andrea Canessa, Manuela Chessa, Fabio Solari, Angel P. del Pobil, Fred H. Hamker, Eris Chinellato, Silvio P. Sabatini:
A Hierarchical System for a Distributed Representation of the Peripersonal Space of a Humanoid Robot. IEEE Trans. Auton. Ment. Dev. 6(4): 259-273 (2014) - 2009
- [j1]Julien Vitay, Jérémy Fix, Fred H. Hamker, Henning Schroll, Frederik Beuth:
Biological Models of Reinforcement Learning. Künstliche Intell. 23(3): 12-18 (2009)
Conference and Workshop Papers
- 2024
- [c7]Zhining Hu, Tobias Schlosser, Michael Friedrich, André Luiz Buarque Vieira e Silva, Frederik Beuth, Danny Kowerko:
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects. ETFA 2024: 1-4 - 2020
- [c6]Nisnab Udas, Frederik Beuth, Danny Kowerko:
Concept Detection in Medical Images using Xception Models - TUCMC at ImageCLEFmed 2020. CLEF (Working Notes) 2020 - [c5]Tobias Schlosser, Frederik Beuth, Danny Kowerko:
Biologically Inspired Hexagonal Deep Learning For Hexagonal Image Generation. ICIP 2020: 848-852 - [c4]Frederik Beuth, Tobias Schlosser, Michael Friedrich, Danny Kowerko:
Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning. IECON 2020: 5323-5330 - 2019
- [c3]Tobias Schlosser, Frederik Beuth, Michael Friedrich, Danny Kowerko:
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks. ETFA 2019: 1511-1514 - 2016
- [c2]Amirhossein Jamalian, Frederik Beuth, Fred H. Hamker:
The Performance of a Biologically Plausible Model of Visual Attention to Localize Objects in a Virtual Reality. ICANN (2) 2016: 447-454 - 2014
- [c1]Frederik Beuth, Amirhossein Jamalian, Fred H. Hamker:
How Visual Attention and Suppression Facilitate Object Recognition? ICANN 2014: 459-466
Informal and Other Publications
- 2024
- [i5]Zhining Hu, Tobias Schlosser, Michael Friedrich, André Luiz Buarque Vieira e Silva, Frederik Beuth, Danny Kowerko:
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects. CoRR abs/2407.20268 (2024) - 2022
- [i4]Tobias Schlosser, Frederik Beuth, Trixy Meyer, Arunodhayan Sampath Kumar, Gabriel Stolze, Olga Furashova, Katrin Engelmann, Danny Kowerko:
Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System. CoRR abs/2204.11970 (2022) - 2021
- [i3]Tobias Schlosser, Frederik Beuth, Danny Kowerko:
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation. CoRR abs/2101.00337 (2021) - [i2]Frederik Beuth, Tobias Schlosser, Michael Friedrich, Danny Kowerko:
Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning. CoRR abs/2102.06955 (2021) - 2019
- [i1]Tobias Schlosser, Frederik Beuth, Michael Friedrich, Danny Kowerko:
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks. CoRR abs/1911.11250 (2019)
Coauthor Index
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