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 | 49 | 2022 |
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 | 43 | 2011 |
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression F Beuth, FH Hamker Vision research 116, 241-257, 2015 | 41 | 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 |
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 | 34 | 2019 |
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 | 32 | 2012 |
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 | 13 | 2020 |
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 |
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 | 8 | 2020 |
Biological models of reinforcement learning J Vitay, J Fix, F Beuth, H Schroll, F Hamker KI-Künstliche Intelligenz, 2009 | 8 | 2009 |
Visual acuity prediction on real-life patient data using a machine learning based multistage system T Schlosser, F Beuth, T Meyer, AS Kumar, G Stolze, O Furashova, ... Scientific Reports 14 (1), 5532, 2024 | 7 | 2024 |
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 | 5 | 2023 |
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 |
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects Z Hu, T Schlosser, M Friedrich, F Beuth, D Kowerko arXiv preprint arXiv:2407.20268, 2024 | 1 | 2024 |
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 | 1 | 2021 |
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 |