Prof. Dr.-Ing. Markus Ulrich
- Machine Vision Metrology
- room: 029
CS 20.40 - phone: +49 721 608-47410
- markus ulrich ∂does-not-exist.kit edu
- www.ipf.kit.edu
- Englerstr. 7
76131 Karlsruhe
Biography
| Since 1.4.2020 | Professor for Machine Vision Metrology at the Institute for Photogrammetry and Remote Sensing of the Karlsruhe Institute of Technology (KIT) |
| 2017 – 2019 | Privatdozent at the KIT-Department of Civil Engineering, Geo and Environmental Sciences |
| 2013 – 2020 | Invention and patent manager at MVTec Software GmbH, Munich |
| 2013 – 2017 | Lecturer for the subject "Industrial Image Processing and Machine Vision " KIT-Department of Civil Engineering, Geo and Environmental Sciences |
| 2008 – 2020 | Head of the research team at MVTec |
| 2005 – 2020 | Lecturer for the subject "Close-Range Photogrammetry" at the Department of Civil, Geo and Environmental Engineering at the Technical University of Munich (TUM) |
| 2003 – 2008 | Software Engineer at MVTec |
| 2000 – 2003 | Research associate and PhD student at the Chair of Photogrammetry and Remote Sensing of the TUM in cooperation with MVTec |
Scientific Qualification
| 1.2.2017 | Postdoctoral lecture qualification (Habilitation) and award of teaching authorization for the subject "Machine Vision" at the KIT-Department of Civil Engineering, Geo and Environmental Sciences |
| 26.6.2003 | PhD degree (Dr.-Ing.) at the TUM Department of Civil, Geo and Environmental Engineering |
| 23.3.2000 | Diploma degree (Dipl.-Ing) in Geodesy |
| 1995 - 2000 | Study of Geodesy at TUM |
Main Research Topics
Machine Vision is a multifaceted discipline and includes aspects from optics (e.g. illumination, lenses), electrical engineering (e.g. sensor technology), mechanical engineering (e.g. industrial robots, optical inspection machines), computer science and software engineering (e.g. efficient implementations of innovative computer vision algorithms). This is also reflected in the research topics:
- Reliable detection and accurate position measurement of objects in images and 3D sensor data
- Camera models and calibration
- Machine learning in industrial applications for object inspection and robotics
- Object identification in images
- Surface inspection of objects in 2D and 3D sensor data
- Hand-eye calibration of industrial robots
In our research work, 20 years of industrial experience for us always represents an important guide and thus promotes the innovative strength of newly developed approaches.
Teaching
Teaching about methods and technologies that are actually used in practice in the professional field is important to provide a sound insight into machine vision metrology. Only in close exchange with the professional field can the requirements of the technologies be sufficiently considered and the students be taught appropriate skills. These requirements are therefore already imparted in the teaching and should play a fundamental and at the same time motivating role. The industrial context is also emphasized by exercises that are relevant in real applications.
The references of machine vision metrology to "classical" geodesy are emphasized and at the same time geodetic aspects (e.g. accuracy considerations and reliability statements) are explicitly considered. Within the framework of research-oriented teaching, students are involved in current research projects at the institute, which also allows scientific methods to be taught.
Activities
Member of the Advisory Board of the VDI/VDE-GMA Department FB 8 "Optical Technologies"
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Co-authership of the VDI-Statusreport
„Maschinelles Lernen in KMU: Künstliche Intelligenz im eigenen Unternehmen nutzen“, VDI-Statusreport November 2020, https://www.vdi.de/ueber-uns/presse/publikationen/details/vdi-statusreport-maschinelles-lernen-in-kmu - Co-authership of the VDI-Statusreport
„Maschinelles Lernen: Künstliche Intelligenz mit neuronalen Netzen in optischen Mess- und Prüfsystemen“, VDI-Statusreport November 2019, https://www.vdi.de/ueber-uns/presse/publikationen/details/kuenstliche-intelligenz-mit-neuronalen-netzen-in-optischen-mess-und-pruefsystemen
Publications
2022
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Implementing machine learning: chances and challenges
Heizmann, M.; Braun, A.; Glitzner, M.; Günther, M.; Hasna, G.; Klüver, C.; Krooß, J.; Marquardt, E.; Overdick, M.; Ulrich, M.
2022. Automatisierungstechnik, 70 (1), 90–101. doi:10.1515/auto-2021-0149 -
Automatic Real-Time Pose Estimation of Machinery from Images
Bertels, M.; Jutzi, B.; Ulrich, M.
2022. Sensors, 22 (7), 2627. doi:10.3390/s22072627
2021
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Geodäsie und Geoinformatik am KIT studieren
Dalheimer, L.; Fuge, R.; Gschwind, C.; Juretzko, M.; Landgraf, S.; Meid, F.; Naab, C.; Ulrich, M.; Weisgerber, J.
2021. doi:10.5445/IR/1000137359 -
Generic Hand–Eye Calibration of Uncertain Robots
Ulrich, M.; Hillemann, M.
2021. IEEE International Conference on Robotics and Automation (ICRA), 30 May-5 June 2021, 11060–11066, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA48506.2021.9560823 -
A Camera Model for Line-Scan Cameras with Telecentric Lenses
Steger, C.; Ulrich, M.
2021. International journal of computer vision, 129, 80–99. doi:10.1007/s11263-020-01358-3
2020
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Artificial intelligence with neural networks in optical measurement and inspection systems
Heizmann, M.; Braun, A.; Hüttel, M.; Klüver, C.; Marquardt, E.; Overdick, M.; Ulrich, M.
2020. Automatisierungstechnik, 68 (6), 477–487. doi:10.1515/auto-2020-0006
2019
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A comparison of shape-based matching with deep-learning-based object detection
Ulrich, M.; Follmann, P.; Neudeck, J.-H.
2019. Technisches Messen, 86 (11), 685–698. doi:10.1515/teme-2019-0076 -
A camera model for cameras with hypercentric lenses and some example applications
Ulrich, M.; Steger, C.
2019. Machine vision and applications, 30 (6), 1013–1028. doi:10.1007/s00138-019-01032-w
2018
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Machine Vision Algorithms and Applications
Steger, C.; Ulrich, M.; Wiedemann, C.
2018. Wiley-VCH Verlag -
MVTec D2S: Densely Segmented Supermarket Dataset
Follmann, P.; Böttger, T.; Härtinger, P.; König, R.; Ulrich, M.
2018. Computer Vision – ECCV 2018. Ed.: V. Ferrari, 581–597, Springer. doi:10.1007/978-3-030-01249-6_35
2017
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Subpixel-Precise Tracking of Rigid Objects in Real-Time
Böttger, T.; Ulrich, M.; Steger, C.
2017. Image Analysis. Part 1. Ed.: P. Sharma, 54–65, Springer Verlag. doi:10.1007/978-3-319-59126-1_5 -
Introducing MVTec ITODD — A Dataset for 3D Object Recognition in Industry
Drost, B.; Ulrich, M.; Bergmann, P.; Härtinger, P.; Steger, C.
2017. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2200–2208, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICCVW.2017.257 -
Object recognition in machine vision. habilitation thesis
Ulrich, M.
2017. Karlsruher Institut für Technologie (KIT)
2016
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Hand-Eye Calibration of SCARA Robots Using Dual Quaternions
Ulrich, M.; Steger, C.
2016. Pattern recognition and image analysis, 16 (1), 231–239. doi:10.1134/S1054661816010272 -
Real-Time Texture Error Detection on Textured Surfaces with Compressed Sensing
Böttger, T.; Ulrich, M.
2016. Pattern recognition and image analysis, 26 (1), 88–94. doi:10.1134/S1054661816010053
2015
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Real-Time Texture Error Detection on Textured Surfaces With Compressed Sensing
Böttger, T.; Ulrich, M.
2015. Proceedings of the OGRW 2014. Ed.: P. Dietrich, 205–210, University of Koblenz-Landau -
Hand-Eye Calibration of SCARA Robots
Ulrich, M.; Heider, A.; Steger, C.
2015. Proceedings of the OGRW2014. 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding. Ed.: D. Paulus, 117–122, University of Koblenz-Landau
2012
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Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition
Ulrich, M.; Wiedemann, C.; Steger, C.
2012. IEEE transactions on pattern analysis and machine intelligence, 34 (10), 1902–1914. doi:10.1109/TPAMI.2011.266
2011
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機器視覺演算法與應用 (Jīqì Shìjué Suàn Fǎ Yǔ Yìngyòng — Machine Vision Algorithms and Applications)
Steger, C.; Ulrich, M.; Wiedemann, C.
2011. Photon-Tech Instruments Co -
Real-time object detection with sub-pixel accuracy using the level set method
Burkert, F.; Butenuth, M.; Ulrich, M.
2011. The photogrammetric record, 26 (134), 154–170. doi:10.1111/j.1477-9730.2011.00633.x
2010
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Model globally, match locally: Efficient and robust 3D object recognition
Drost, B.; Ulrich, M.; Navab, N.; Ilic, S.
2010. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 998–1005, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CVPR.2010.5540108 -
Evaluation of efficient methods for optical flow computation - Evaluierung effizienter Methoden zur Berechnung des optischen Flusses
Frey, D.; Ulrich, M.; Hinz, S.
2010. Photogrammetrie - Fernerkundung - Geoinformation, 10 (1), 5–16. doi:10.1127/1432-8364/2010/0036
2009
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CAD-Based Recognition of 3D Objects in Monocular Images
Ulrich, M.; Wiedemann, C.; Steger, C.
2009. IEEE International Conference on Robotics and Automation, 1191–1198, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ROBOT.2009.5152511
2008
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画像処理アルゴリズムと実践アプリケーション (Gazou Shori Algorithm to Jissen Application — Image Processing Algorithms and Applications)
Steger, C.; Ulrich, M.; Wiedemann, C.
2008. LinX Corporation -
机器视觉算法与应用 (Jīqì Shìjué Suàn Fǎ Yǔ Yìngyòng — Machine Vision Algorithms and Applications)
Steger, C.; Ulrich, M.; Wiedemann, C.
2008. Tsinghua University Press -
Recognition and Tracking of 3D Objects
Wiedemann, C.; Ulrich, M.; Steger, C.
2008. Pattern Recognition. Ed.: G. Rigoll, 132–141, Springer Verlag. doi:10.1007/978-3-540-69321-5_14
2007
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Machine Vision Algorithms and Applications
Steger, C.; Ulrich, M.; Wiedemann, C.
2007. Wiley-VCH Verlag
2004
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Erkennung von zusammengesetzten Objekten in Bildern unter Echtzeit-Anforderungen
Ulrich, M.; Steger, C.; Baumgartner, A.; Ebner, H.
2004. Commemorative Volume for the 60th Birthday of Prof. Dr. Armin Grün, ETH Zürich, 251–259, Institute of Geodesy and Photogrammetry -
Erkennung von zusammengesetzten Objekten in Bildern unter Echtzeit-Anforderungen
Ulrich, M.; Steger, C.; Baumgartner, A.; Ebner, H.
2004. ZfV, 129 (3), 184–194
2003
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Hierarchical Real-Time Recognition of Compound Objects in Images
Ulrich, M.
2003. Verlag der Bayerischen Akademie der Wissenschaften in Kommission beim Verlag C.H. Beck -
Real-time object recognition using a modified generalized Hough transform
Ulrich, M.; Steger, C.; Baumgartner, A.
2003. Pattern recognition, 36 (11), 2557–2570. doi:10.1016/S0031-3203(03)00169-9
2002
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Empirical Performance Evaluation of Object Recognition Methods
Ulrich, M.; Steger, C.
2002. Empirical Evaluation Methods in Computer Vision. Ed.: H.I. Christensen, 62–76, World Scientific Publishing -
Performance Evaluation of 2D Object Recognition Techniques
Ulrich, M.; Steger, C.
2002. Technische Universität München (TUM) -
Automatic Hierarchical Object Decomposition for Object Recognition
Ulrich, M.; Baumgartner, A.; Steger, C.
2002. The international archives of photogrammetry, remote sensing and spatial information sciences, XXXIV-5/WGV/1, 99–104 -
Performance Comparison of 2D Object Recognition Techniques
Ulrich, M.; Steger, C.
2002. Proceedings of the ISPRS Commission III Symposium Photogrammetric Computer Vision, 368–374 -
Vorhersage der Erdorientierungs-Parameter unter Verwendung künstlicher Neuronaler Netze
Schuh, H.; Ulrich, M.; Egger, D.; Müller, J.; Schwegmann, W.
2002. Vorträge beim 4. DFG-Rundgespräch im Rahmen des Forschungsvorhabens Rotation der Erde zum Thema ’Wechselwirkungen im System Erde’. Ed.: H. Schuh, 87–89, Verlag der Bayerischen Akademie der Wissenschaften -
Prediction of Earth orientation parameters by artificial neural networks
Schuh, H.; Ulrich, M.; Egger, D.; Müller, J.; Schwegmann, W.
2002. Journal of geodesy, 76 (5), 247–258. doi:10.1007/s00190-001-0242-5
2001
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Prediction of Earth Orientation Parameters by Artificial Neural Networks
Schuh, H.; Ulrich, M.
2001. Journées Systèmes de Référence Spatio-Temporels : Paris, France, 18 - 20 Septembre 2000 ; J2000, une époque fondamentale pour les origines des systèmes de référence. [J2000, a fundamental epoch for origins of reference systems and astronomical models]. Ed.: N. Capitaine, 302–303, Observatoire de Paris -
Real-Time Object Recognition in Digital Images for Industrial Applications
Ulrich, M.; Steger, C.; Baumgartner, A.; Ebner, H.
2001. Technische Universität München (TUM) -
Real-Time Object Recognition Using a Modified Generalized Hough Transform
Ulrich, M.; Steger, C.; Baumgartner, A.; Ebner, H.
2001. Photogrammetrie - Fernerkundung - Geoinformation: Geodaten schaffen Verbindungen. Hrsg.: E. Seyfert, 571–578, DGPF -
新実践画像処理 (Shin Jissen Gazou Shori — Practical Image Processing, 2nd Edition)
Koshimizu, H.; Ishii, A.; Suga, Y.; Kaneko, S.; Hara, Y.; Murakami, K.; Umeda, K.; Murakami, N.; Tsujitani, J.; Bushimata, S.; Hirata, A.; Adachi, T.; Eckstein, W.; Steger, C.; Lückenhaus, M.; Ulrich, M.; Blahusch, G.
2001. LinX Corporation -
Real-Time Object Recognition in Digital Images for Industrial Applications
Ulrich, M.; Steger, C.; Baumgartner, A.; Ebner, H.
2001. Optical 3-D Measurement Techniques V. Ed.: A. Grün, 308–318, Vienna University of Technology
2000