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Prof. Dr. Thomas Kopinski

FB Ingenieur- und Wirtschaftswissenschaften | Meschede

 Campus Gebäude

Machine Learning | Data Science

Interessensgebiete

  • Machine Learning | Deep Learning
  • Data-driven Applications
  • 3D Programming (Unreal Engine)
  • Explainable AI
  • Alignment Algorithms

Forschungsprojekte

Sprechstunde

nach Vereinbarung per E-Mail


Weitere Infos

Vita

Beruflicher Werdegang ab Examen

2006
Diplom in Informatik an der TU Dortmund

2006 - 2011
Software-Entwicklung für Mobile Plattformen

2015
Promotion zum Dr. an der Université Paris-Saclay

2017
Professor für Ingenieurinformatik an der Fachhochschule Südwestfalen, Standort Meschede

Publikationen

[1] T. Kopinski, S. Geisler, and U. Handmann, “Contactless interaction for automotive applications,” Mensch & Computer 2013-Workshopband, 2013.

[2] T. Kopinski, A. Gepperth, S. Geisler, and U. Handmann, “Neural network based data fusion for hand pose recognition with multiple tof sensors,” ICANN, 2014.

[3] T. Kopinski, S. Geisler, and U. Handmann, “Demonstrator f¨ur ein handgestenbasiertes interaktionskonzept im automobil,” in Mensch & Computer 2014–Workshopband: 14. Fachübergreifende Konferenz für Interaktive und Kooperative Medien–Interaktiv unterwegs-Freiräume gestalten, p. 205, Walter de Gruyter GmbH & Co KG, 2014.

[4] T. Kopinski, D. Malysiak, A. Gepperth, and U. Handmann, “Time-offlight based multi-sensor fusion strategies for hand gesture recognition,” in Computational Intelligence and Informatics (CINTI), 2014 IEEE 15th International Symposium on, IEEE, 2014.

[5] M. Lefort, T. Kopinski, and A. Gepperth, “Multimodal space representation driven by self-evaluation of predictability,” in ICDL-EPIROB-The fourth joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, 2014.

[6] T. Kopinski, S. Geisler, L.-C. Caron, A. Gepperth, and U. Handmann, “A real-time applicable 3d gesture recognition system for automobile hmi,” in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, pp. 2616–2622, IEEE, 2014.

[7] T. Kopinski, A. Gepperth, and U. Handmann, “A simple technique for improving multi-class classification with neural networks,” ESANN, 2015.

[8] T. Kopinski, S. Magand, A. Gepperth, and U. Handmann, “A light-weight real-time applicable hand gesture recognition system for automotive applications,” in Intelligent Vehicles Symposium (IV), 2015 IEEE, pp. 336–342, IEEE, 2015.

[9] T. Kopinski, S. Magand, U. Handmann, and A. Gepperth, “A pragmatic approach to multi-class classification,” in Neural Networks (IJCNN), 2015 International Joint Conference on, pp. 1–8, IEEE, 2015.

[10] T. Kopinski, A. Gepperth, and U. Handmann, “A real-time applicable dynamic hand gesture recognition framework,” in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, pp. 2358–2363, IEEE, 2015.

[11] T. Kopinski and U. Handmann, “Touchless interaction for future mobile applications,” in Computing, Networking and Communications (ICNC), 2016 International Conference on, pp. 1–6, IEEE, 2016.

[12] T. Kopinski, A. Gepperth, and U. Handmann, “A time-of-flight-based hand posture database for human-machine interaction,” in International Conference on Automation, Robotics and Computer Vision. IEEE, 2016.

[13] T. Kopinski, F. Sachara, A. Gepperth, and U. Handmann, “A deep learning approach for hand posture recognition from depth data,” in International Conference on Artificial Neural Networks, pp. 179–186, Springer, 2016.

[14] T. Kopinski, F. Sachara, and U. Handmann, “A deep learning approach to mid-air gesture interaction for mobile devices from time-of-flight data,” in Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 1–9, ACM, 2016.

[15] T. Kopinski, J. Eberwein, S. Geisler, and U. Handmann, “Touch versus mid-air gesture interfaces in road scenarios-measuring driver performance degradation,” in Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on, pp. 661–666, IEEE, 2016.

[16] T. Kopinski, Neural Learning Methods for Human-Computer Interaction. PhD thesis, Université Paris-Saclay, 2016.

[17] F. Sachara, F. Handmann, N. Cremer, T. Kopinski, A. Gepperth, and U. Handmann, “A large-scale multi-pose 3d-rgb object database,” in Neural Networks (IJCNN), 2017 International Joint Conference on, pp. 1326–1332, IEEE, 2017.

[18] T. Kopinski, “Dynamic hand gesture recognition for mobile systems using deep lstm,” in Intelligent Human Computer Interaction: 9th International Conference, IHCI 2017, Evry, France, December 11-13, 2017, Proceedings, vol. 10688, p. 19, Springer, 2017.

[19] F. Sachara, T. Kopinski, A. Gepperth, and U. Handmann, “Free-hand gesture recognition with 3d-cnns for in-car infotainment control in real-time,” in Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, pp. 959–964, IEEE, 2017.

[20] A. Gepperth, A. Sarkar, and T. Kopinski, “An energy-based convolutional som model with self-adaptation capabilities,” in International Conference on Artificial Neural Networks, pp. 422–433, Springer, 2018.

[21] N. Zengeler, T. Kopinski, and U. Handmann, “Hand gesture recognition in automotive human–machine interaction using depth cameras,” Sensors, vol. 19, no. 1, p. 59, 2019.

[22] T. Kopinski, R. Luz Y Graf, F. Neubürger, T. Zabel, and J. Stadler, “A machine learning approach for object detection from image data in crash test applications,” in Crash Tech, Crash Tech, 2020.

[23] F. Neubürger, Y. Saeid, and T. Kopinski, “Variational-autoencoder architectures for anomaly detection in industrial processes,” in ICDM, ICDM, 2021.

[24] A. Gepperth and T. Kopinski, “Gesture mnist: A new free-hand gesture dataset,” in Neural Networks (IJCNN), 2022 International Joint Conference on, IEEE, 2022 - eingereicht.

[25] D. Gierse, F. Neubürger, and T. Kopinski, “Deep learning fusion techniques for robust object detection,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, p. ..., 2022 - eingereicht.

[26] Y. Saeid, F. Neubürger, and T. Kopinski, “Synchronized vqgan - a novel technique for high-resolution image creation,” in European conference on computer vision, p. ..., Springer, 2022 - Einreichung geplant.

Team
Das Team von Prof. Dr. Thomas Kopinski

Felix Neubürger

  • Continuous Learning / Bayesian Deep Learning for Predictive Maintenance
  • PhD Candidate in Applied Computer Science | TU Darmstadt
Zur Seite von Felix Neubürger

Yasser Saeid

  • Machine Learning / Deep Learning
  • PhD Candidate in Machine Learning
Zur Seite von Yasser Saeid

Daniel Gierse

  • Machine Learning
  • Unreal Development
external content (Youtube)

Within the scope of a student thesis, the object recognition and tracking of objects in high-resolution videos was investigated. The software developed here will be used in the near future by the project partner BMW AG in passive safety.

external content (Youtube)

In preparation for future mixed reality projects, a test environment was created for displaying architectural models within a city environment. This city environment is modeled from publicly available environmental data.