| Split of spatial attention as predicted by a systems‐level model of visual attention M Zirnsak, F Beuth, FH Hamker European Journal of Neuroscience 33 (11), 2035-2045, 2011 | 44 | 2011 |
| A mechanistic cortical microcircuit of attention for amplification, normalization and suppression F Beuth, FH Hamker Vision research 116, 241-257, 2015 | 39 | 2015 |
| A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot M Antonelli, A Gibaldi, F Beuth, AJ Duran, A Canessa, M Chessa, F Solari, ... IEEE Transactions on Autonomous Mental Development 6 (4), 259-273, 2014 | 38 | 2014 |
| Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks T Schlosser, M Friedrich, F Beuth, D Kowerko Journal of Intelligent Manufacturing, 1-25, 2022 | 31 | 2022 |
| Comparison of GPU-and CPU-implementations of mean-firing rate neural networks on parallel hardware HÜ Dinkelbach, J Vitay, F Beuth, FH Hamker Network: Computation in Neural Systems 23 (4), 212-236, 2012 | 30 | 2012 |
| A novel visual fault detection and classification system for semiconductor manufacturing using stacked hybrid convolutional neural networks T Schlosser, F Beuth, M Friedrich, D Kowerko 2019 24th IEEE International Conference on Emerging Technologies and Factory …, 2019 | 26 | 2019 |
| Visual attention in primates and for machines-neuronal mechanisms DIF Beuth | 9 | 2019 |
| Attentive stereoscopic object recognition F Beuth, J Wiltschut, F Hamker Workshop New Challenges in Neural Computation 2010, 41, 2010 | 9 | 2010 |
| Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning F Beuth, T Schlosser, M Friedrich, D Kowerko IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics …, 2020 | 8 | 2020 |
| Biologically Inspired Hexagonal Deep Learning For Hexagonal Image Generation T Schlosser, F Beuth, D Kowerko 2020 IEEE International Conference on Image Processing (ICIP), 848-852, 2020 | 6 | 2020 |
| Biological models of reinforcement learning J Vitay, J Fix, F Beuth, H Schroll, F Hamker KI-Künstliche Intelligenz, 2009 | 6 | 2009 |
| Attention as cognitive, holistic control of the visual system F Beuth, FH Hamker, T Villmann, FM Schleif Proc Workshop New Challenges in Neural Computation, 133-140, 2015 | 5 | 2015 |
| The performance of a biologically plausible model of visual attention to localize objects in a virtual reality A Jamalian, F Beuth, FH Hamker International Conference on Artificial Neural Networks, 447-454, 2016 | 4 | 2016 |
| How Visual Attention and Suppression Facilitate Object Recognition? F Beuth, A Jamalian, FH Hamker International Conference on Artificial Neural Networks, 459-466, 2014 | 2 | 2014 |
| A large-scale neurocomputational model of spatial cognition integrating memory with vision M Burkhardt, J Bergelt, L Gönner, HÜ Dinkelbach, F Beuth, A Schwarz, ... Neural Networks, 2023 | 1 | 2023 |
| Concept Detection in Medical Images using Xception Models-TUCMC at ImageCLEFmed 2020. N Udas, F Beuth, D Kowerko CLEF (Working Notes), 2020 | 1 | 2020 |
| The guidance of vision while learning categories F Beuth, FH Hamker Poster on the Bernstein Conference on Computational Neuroscience (BCCN), 2008 | 1 | 2008 |
| Contrasting attentional processing in visual search, object recognition, and complex tasks F Beuth, D Kowerko, FH Hamker Journal of Vision 21 (9), 2753-2753, 2021 | | 2021 |
| Schlussbericht zum InnoProfile-Transfer Begleitprojekt localizeIT R Manthey, T Kretzschmar, F Schmidsberger, H Hussein, R Erler, ... Technische Universität Chemnitz, Fakultät für Informatik, 2020 | | 2020 |
| Learning Object Representations for Modeling Attention in Real World Scenes A Schwarz, F Beuth, FH Hamker | | 2016 |
