| 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 33 (4), 1099–1123, 2022 | 32 | 2022 |
| 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 |
| Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework T Schlosser, M Friedrich, D Kowerko 2019 18th IEEE International Conference on Machine Learning and Applications …, 2019 | 20 | 2019 |
| 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 2020 46th Annual Conference of the IEEE Industrial Electronics Society …, 2020 | 8 | 2020 |
| Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation T Schlosser, F Beuth, D Kowerko 2020 27th IEEE International Conference on Image Processing (ICIP), 848–852, 2020 | 6 | 2020 |
| Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System T Schlosser, F Beuth, T Meyer, A Sampath Kumar, G Stolze, O Furashova, ... Scientific Reports 14 (1), 5532, 2024 | 4 | 2024 |
| Generation of Images with Hexagonal Tessellation using Common Digital Cameras R Manthey, T Schlosser, D Kowerko IBS International Summerschool on Computer Science, Computer Engineering and …, 2017 | 4 | 2017 |
| Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multi-Scale Encoders and Decoders A Sampath Kumar, T Schlosser, H Langner, M Ritter, D Kowerko Bioengineering 10 (10), 1177, 2023 | 2 | 2023 |
| A Consolidated Overview of Evaluation and Performance Metrics for Machine Learning and Computer Vision T Schlosser, M Friedrich, T Meyer, D Kowerko https://www.researchgate.net/publication …, 2023 | 2 | 2023 |
| Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study AL Vieira e Silva, F Simões, D Kowerko, T Schlosser, F Battisti, ... 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV …, 2024 | 1* | 2024 |
| Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Processing With The Hexagonal Image Processing Framework Hexnet T Schlosser, M Friedrich, T Meyer, D Kowerko, M Eibl https://www.researchgate.net/publication …, 2022 | 1 | 2022 |
| Entwurf und Implementierung von Optimierungs- und Funktionserweiterungen der hexagonalen Bildrasterung in der Videokompressionssoftware x264HMod T Schlosser, R Manthey, M Ritter Studierendensymposium Informatik 2016 der TU Chemnitz, 63–74, 2016 | 1 | 2016 |
| Simulation of Semiconductor Wafer Dicing Induced Faults on Chips and Their Application as Augmentation Method for a Deep Learning Based Visual Inspection System M Friedrich, T Schlosser, D Kowerko https://www.researchgate.net/publication …, 2024 | | 2024 |
| A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts R Vogel, T Schlosser, R Manthey, M Ritter, M Vodel, M Eibl, KA Schneider Machine Learning and Knowledge Extraction 6 (2), 800–826, 2024 | | 2024 |
| Improving Learning-Based Birdsong Classification by Utilizing Combined Audio Augmentation Strategies A Sampath Kumar, T Schlosser, S Kahl, D Kowerko https://www.researchgate.net/publication/379513673_Improving_Learning …, 2023 | | 2023 |
| Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study AL Vieira e Silva, F Simões, D Kowerko, T Schlosser, F Battisti, ... 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2023 | | 2023 |
| Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning – Extended arXiv Version F Beuth, T Schlosser, M Friedrich, D Kowerko https://arxiv.org/abs/2102.06955, 2021 | | 2021 |
