| Hand gesture recognition in automotive human–machine interaction using depth cameras N Zengeler, T Kopinski, U Handmann Sensors 19 (1), 59, 2018 | 81 | 2018 |
| Dynamic hand gesture recognition for mobile systems using deep LSTM A Sarkar, A Gepperth, U Handmann, T Kopinski Intelligent Human Computer Interaction: 9th International Conference, IHCI …, 2017 | 31 | 2017 |
| Free-hand gesture recognition with 3D-CNNs for in-car infotainment control in real-time F Sachara, T Kopinski, A Gepperth, U Handmann 2017 IEEE 20th International Conference on Intelligent Transportation …, 2017 | 20 | 2017 |
| A pragmatic approach to multi-class classification T Kopinski, S Magand, U Handmann, A Gepperth 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 20 | 2015 |
| A real-time applicable 3d gesture recognition system for automobile hmi T Kopinski, S Geisler, LC Caron, A Gepperth, U Handmann 17th International IEEE Conference on Intelligent Transportation Systems …, 2014 | 16 | 2014 |
| Touch versus mid-air gesture interfaces in road scenarios-measuring driver performance degradation T Kopinski, J Eberwein, S Geisler, U Handmann 2016 IEEE 19th International Conference on Intelligent Transportation …, 2016 | 14 | 2016 |
| Neural network based data fusion for hand pose recognition with multiple tof sensors T Kopinski, A Gepperth, S Geisler, U Handmann International Conference on Artificial Neural Networks, 233-240, 2014 | 14 | 2014 |
| Gesture-based human-machine interaction for assistance systems T Kopinski, S Geisler, U Handmann 2015 IEEE International Conference on Information and Automation, 510-517, 2015 | 12 | 2015 |
| A light-weight real-time applicable hand gesture recognition system for automotive applications T Kopinski, S Magand, A Gepperth, U Handmann 2015 IEEE Intelligent Vehicles Symposium (IV), 336-342, 2015 | 12 | 2015 |
| Das hybride Büro: Gestaltungsansätze für New Work-Arbeitsumgebungen anhand eines Fallbeispiels C Reindl, R Lanwehr, T Kopinski Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte …, 2022 | 11 | 2022 |
| A simple technique for improving multi-class classification with neural networks. T Kopinski, A Gepperth, U Handmann ESANN, 2015 | 11 | 2015 |
| A time-of-flight-based hand posture database for human-machine interaction T Kopinski, A Gepperth, U Handmann 2016 14th International Conference on Control, Automation, Robotics and …, 2016 | 9 | 2016 |
| Time-of-flight based multi-sensor fusion strategies for hand gesture recognition T Kopinski, D Malysiak, A Gepperth, U Handmann 2014 IEEE 15th International Symposium on Computational Intelligence and …, 2014 | 8 | 2014 |
| Touchless interaction for future mobile applications T Kopinski, U Handmann 2016 International Conference on Computing, Networking and Communications …, 2016 | 7 | 2016 |
| User expectations on touchless gestures in vehicles P März, D Schwahlen, S Geisler, T Kopinski Gesellschaft für Informatik eV, 2016 | 6 | 2016 |
| A deep learning approach to mid-air gesture interaction for mobile devices from time-of-flight data T Kopinski, F Sachara, U Handmann Proceedings of the 13th International Conference on Mobile and Ubiquitous …, 2016 | 5 | 2016 |
| A real-time applicable dynamic hand gesture recognition framework T Kopinski, A Gepperth, U Handmann 2015 IEEE 18th International Conference on Intelligent Transportation …, 2015 | 5 | 2015 |
| Multimodal space representation driven by self-evaluation of predictability M Lefort, T Kopinski, A Gepperth 4th International Conference on Development and Learning and on Epigenetic …, 2014 | 5 | 2014 |
| Contactless Interaction for Automotive Applications. T Kopinski, S Geisler, U Handmann MuC Workshopband, 87-94, 2013 | 5 | 2013 |
| A deep learning approach for hand posture recognition from depth data T Kopinski, F Sachara, A Gepperth, U Handmann Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 4 | 2016 |
