Prof. Dr.-Ing. Markus Ulrich

Englerstr. 7
76131 Karlsruhe

Professorship for Machine Vision Metrology

Biography

Scientific Qualification

Main Research Topics

Teaching

Activities

Publications

Patents

Others

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

Our research work often focuses on geodetic aspects such as reliability or accuracy. At the same time, 20 years of industrial experience serves as an important guide and ensures the innovative power of the newly developed processes.

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 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

Chair of the ISPRS Working Group II/9 "Vision Metrology"
Editor of the DGPF Journal PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science for Photogrammetry
Activities in VDI - The Association of German Engineers
- Member of the VDI/VDE-GMA Department FB 1 "Methodik der Mess- und Sensortechnik"
- Chair of the VDI/VDE-GMA Fachausschusses FA 1.13 "Neuronale Netze in der Sensordatenverarbeitung"
- Member VDI/VDE-GMA Fachausschuss FA 1.21 "Bildverarbeitung in Mess- u. Automatisierungstechnik"
- Co-authership of the VDI-Statusreport
  „Maschinelles Lernen in KMU: Künstliche Intelligenz im eigenen Unternehmen nutzen“, 2020
- Co-authership of the VDI-Statusreport
  „Maschinelles Lernen: Künstliche Intelligenz mit neuronalen Netzen in optischen Mess- und Prüfsystemen“, 2019

Scientific Publications

2024

Vision-guided robot calibration using photogrammetric methods
Ulrich, M.; Steger, C.; Butsch, F.; Liebe, M.
2024. ISPRS Journal of Photogrammetry and Remote Sensing, 218 (Part A), 645–662. doi:10.1016/j.isprsjprs.2024.09.037
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2024. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 223–230. doi:10.5194/isprs-annals-X-2-2024-223-2024
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Hillemann, M.; Langendörfer, R.; Heiken, M.; Mehltretter, M.; Schenk, A.; Weinmann, M.; Hinz, S.; Heipke, C.; Ulrich, M.
2024. arxiv. doi:10.48550/arXiv.2405.04345
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2024. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92 (2), 101–114. doi:10.1007/s41064-024-00280-4
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Hillemann, M.; Langendörfer, R.; Heiken, M.; Mehltretter, M.; Schenk, A.; Weinmann, M.; Hinz, S.; Heipke, C.; Ulrich, M.
2024. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2-2024, 137–144. doi:10.5194/isprs-archives-XLVIII-2-2024-137-2024
Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2024. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-2-2024, 129–136. doi:10.5194/isprs-annals-X-2-2024-129-2024
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Landgraf, S.; Hillemann, M.; Kapler, T.; Ulrich, M.
2024. arxiv. doi:10.48550/arXiv.2402.10580
Uncertainty-Aware Hand–Eye Calibration
Ulrich, M.; Hillemann, M.
2024. IEEE Transactions on Robotics, 40, 573–591. doi:10.1109/TRO.2023.3330609

2023

Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield
Wolf, D. W.; Ulrich, M.; Kapoor, N.
2023. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, 25th December 2023, 4102–4111, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICCVW60793.2023.00443
Novel developments of refractive power measurement techniques in the automotive world
Wolf, D. W.; Ulrich, M.; Braun, A.
2023. Metrologia, 60 (6), Article no: 064001. doi:10.1088/1681-7575/acf1a4
Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient
Werner Wolf, D.; Ulrich, M.; Braun, A.
2023. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 5190–5197, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC57777.2023.10421970
A comparison of learning-based approaches for the corrosion detection on barrels in industrial applications
Haitz, D.; Hübner, P.; Ulrich, M.; Jutzi, B.
2023. tm - Technisches Messen, 90 (7-8), 522–532. doi:10.1515/teme-2023-0009
Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient
Wolf, D. W.; Ulrich, M.; Braun, A.
2023. arxiv. doi:10.48550/arXiv.2305.14513
U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation
Landgraf, S.; Hillemann, M.; Wursthorn, K.; Ulrich, M.
2023. arxiv. doi:10.48550/arXiv.2307.09947
Segmentation of industrial burner flames: a comparative study from traditional image processing to machine and deep learning
Landgraf, S.; Hillemann, M.; Aberle, M.; Jung, V.; Ulrich, M.
2023. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 953–960. doi:10.5194/isprs-annals-X-1-W1-2023-953-2023
Sensitivity Analysis of AI-Based Algorithms for Autonomous Driving on Optical Wavefront Aberrations Induced by the Windshield
Wolf, D. W.; Ulrich, M.; Kapoor, N.
2023. doi:10.5445/IR/1000162967
Combining Hololens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping
Haitz, D.; Jutzi, B.; Ulrich, M.; Jäger, M.; Hübner, P.
2023. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W1-2023, 167 – 174. doi:10.5194/isprs-archives-XLVIII-1-W1-2023-167-2023
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning
Landgraf, S.; Hillemann, M.; Aberle, M.; Jung, V.; Ulrich, M.
2023. doi:10.5445/IR/1000159876
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
Landgraf, S.; Wursthorn, K.; Hillemann, M.; Ulrich, M.
2023
Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping
Haitz, D.; Jutzi, B.; Ulrich, M.; Jäger, M.; Huebner, P.
2023. arxiv. doi:10.48550/arXiv.2304.14301

2022

Semantic segmentation with small training datasets: A case study for corrosion detection on the surface of industrial objects
Haitz, D.; Hübner, P.; Ulrich, M.; Landgraf, S.; Jutzi, B.
2022. Forum Bildverarbeitung 2022. Ed.: T. Längle; M. Heizmann, 73–85, KIT Scientific Publishing
Corrosion detection for industrial objects: from multi-sensor system to 5D feature space
Haitz, D.; Jutzi, B.; Hübner, P.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B1-2022, 143–150. doi:10.5194/isprs-archives-XLIII-B1-2022-143-2022
Evaluation of self-supervised learning approaches for semantic segmentation of industrial burner flames
Landgraf, S.; Kühnlein, L.; Hillemann, M.; Hoyer, M.; Keller, S.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 601–607. doi:10.5194/isprs-archives-XLIII-B2-2022-601-2022
Comparison of uncertainty quantification methods for CNN-based regression
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 721–728. doi:10.5194/isprs-archives-XLIII-B2-2022-721-2022
Automatic Real-Time Pose Estimation of Machinery from Images
Bertels, M.; Jutzi, B.; Ulrich, M.
2022. Sensors, 22 (7), 2627. doi:10.3390/s22072627
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
A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses
Steger, C.; Ulrich, M.
2022. Journal of Mathematical Imaging and Vision, 64, 105–130. doi:10.1007/s10851-021-01055-x

2021

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

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

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

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

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

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

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

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

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

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

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

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

Machine Vision Algorithms and Applications
Steger, C.; Ulrich, M.; Wiedemann, C.
2007. Wiley-VCH Verlag

2004

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

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
Hierarchical Real-Time Recognition of Compound Objects in Images. PhD dissertation
Ulrich, M.
2003
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

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

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

Vorhersage der Erdrotationsparameter mit Hilfe Neuronaler Netze. diploma thesis
Ulrich, M.
2000

Further Publications

Dataset for the Segmentation of Industrial Burner Flames
Landgraf, S.; Hillemann, M.; Ulrich, M.; Aberle, M.; Jung, V.
2023, June 20. doi:10.5445/IR/1000159497

Beat the MArVIN - Industrieroboter MArVIN lernt Geschicklichkeitsspiel "der heiße Draht"
Wursthorn, K.; Hillemann, M.; Ulrich, M.
2022. doi:10.5445/IR/1000150338

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

Patents

Hand-eye calibration of camera-guided devices
- EP 4094897 (publication date: 20.9.2023)
System and method for model adaptation
- JP 6612822 (publication date: 27.11.2019)
- US 10460472 (publication date: 29.10.2019)
System and method for efficient 3D reconstruction of objects with telecentric line-scan cameras
- JP 7486740 (publication date: 20.5.2024)
- EP 3896640 (publication date: 28.9.2022)
- US 11328478 (publication date: 10.5.2022)
Recognition and pose determination of 3D objects in multimodal scenes
- JP 6216508 (publication date: 18.10.2017)
- CN 103729643 (publication date: 12.09.2017)
- US 8994723 (publication date: 31.5.2015)
- EP 2720171 (publication date: 8.4.2015)
Recognition and pose determination of 3D objects in 3D scenes
- CN 102236794 (publication date: 4.3.2015)
- JP 5677798 (publication date: 25.2.2015)
- US 8830229 (publication date: 9.9.2014)
- EP 2385483 (publication date: 21.11.2012)
System and method for 3D object recognition
- CN 101408931 (publication date: 20.2.2013)
- US 8379014 (publication date: 19.2.2013)
- EP 2048599 (publication date: 16.12.2009)
- JP 4785880 (publication date: 30.4.2009)
System and methods for automatic parameter determination in machine vision
- US 7953290 (publication date: 31.5.2011)
- US 7953291 (publication date: 31.5.2011)
- US 7751625 (publication date: 6.6.2010)
- JP 4907219 (publication date: 4.10.2007)
Hierarchical component-based object recognition
- JP 5330579 (publication date: 1.11.2012)
- EP 1394727 (publication date: 12.10.2011)
- JP 4334301 (publication date: 30.9.2009)
- JP 5329254 (publication date: 14.5.2009)
- US 7239929 (publication date: 3.7.2007)

Others

Landscape Photography