Biblio

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1
Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.
3
Caputo, M., Denker K., Dums B., & Umlauf G. (2012).  3d hand gesture recognition based on sensor fusion of commodity hardware. (Reiterer, H., & Deussen O., Ed.).Mensch und Computer. PDF icon GestureRecognition.pdf (378.26 KB)
Hoher, P., Baur T., Reuter J., Griesser D., Govaers F., & Koch W. (2024).  3D-Extended Object Tracking and Shape Classification with a Lidar Sensor using Random Matrices and Virtual Measurement Models. 27th International Conference on Information Fusion (FUSION). 1-8.
A
Scharpf, P., Hong C. Lap, & Duerr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398-404.
Scharpf, P., Hong C. Lap, & Dürr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398–404.
Denker, K., & Umlauf G. (2011).  An accurate real-time multi-camera matching on the GPU for 3d reconstruction. Journal of WSCG. 19, 9-16.PDF icon RealTimeMultiCamera.pdf (770.39 KB)
Burkhart, D., Hamann B., & Umlauf G. (2010).  Adaptive and feature-preserving subdivision for high-quality tetrahedral meshes. Computer Graphics Forum. 29, 117-127.PDF icon AdaptiveSubTetraMeshes.pdf (1022.53 KB)
Constantiniu, A., Steinmann P., Bobach T., Farin G., & Umlauf G. (2008).  The adaptive Delaunay tesselation: A neighborhood covering meshing technique. Computational Mechanics. 42, 655-669.PDF icon AdaptDelTess.pdf (1.33 MB)
Burkhart, D., Hamann B., & Umlauf G. (2010).  Adaptive tetrahedral subdivision for finite element analysis. (.N., N., Ed.).Computer Graphics International, Singapore 2010. PDF icon TetraSubFEA.pdf (3.43 MB)
Franz, M. O., Schölkopf B., Mallot H. A., & Bülthoff H. H. (1996).  Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten. (Thielscher, M., & Bornscheuer S.-E., Ed.).Fortschritte der {Künstlichen Intelligenz}. 92.PDF icon Franz et al._1996_Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.pdf (63.1 KB)
Umlauf, G. (2005).  Analysis and tuning of subdivision schemes. (Jüttler, B., Ed.).Proceedings of Spring Conference on Computer Graphics SCCG 2005. PDF icon ATSubSchemes.pdf (765.94 KB)
Ginkel, I., & Umlauf G. (2007).  Analyzing a generalized Loop subdivision scheme. Computing. 79, 353-363.PDF icon AnalyzeSubScheme.pdf (190.38 KB)
Seepold, R., Dermati C., Kostka A., Pfeil L., Lange R., Hermann M., et al. (2016).  Analyzing environmental conditions and vital signs to increase healthy living. Mobile Networks for Biometric Data Analysis.
Umlauf, G. (2000).  Analyzing the characteristic map of triangular subdivision schemes. Constructive Approximation. 16, 145-155.PDF icon LoopCharMap.pdf (431.79 KB)
Stadelmann, T., Stockinger K., Braschler M., Cieliebak M., Baudinot G., Dürr O., et al. (2013).  Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit. Amsterdam, Netherlands.
Baumgartner, C., Bohnet D., Meyer S., Leber A., & Ramadane M. (2025).  Assessing the Environmental Impact of IoT Devices - Hotspots and Guidelines for a Better Understanding. 2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC). 1–11.
Casanova, R., Murina E., Haberecker M., Honcharova-Biletska H., Vrugt B., Dürr O., et al. (2018).  Automatic classification of non-small cell lung cancer histologic sub-types by deep learning. VIRCHOWS ARCHIV. 108-108.
B
Yovel, Y., Stilz P., Melcón M. L., Franz M. O., & Schnitzler H.-U. (2008).  Bats can use echolocation calls for individual recognition. {Proc. Sensory coding and the natural environment 2008}.
Dürr, O., Fan P-Y., & Yin Z-X. (2023).  Bayesian Calibration of MEMS Accelerometers. IEEE Sensors Journal.
Dold, D., Ruegamer D., Sick B., & Dürr O. (2024).  Bayesian Semi-structured Subspace Inference. (Dasgupta, S., Mandt S., & Li Y., Ed.).Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. 1819–1827.
Dürr, O., Hörtling S., Dold D., Kovylov I., & Sick B. (2024).  Bernstein flows for flexible posteriors in variational Bayes. AStA Advances in Statistical Analysis. 108, 375–394.
Stadelmann, T., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
Grunwald, M., Hermann M., Freiberg F., & Franz M. O. (2021).  Biologically-inspired vs. CNN texture representations in novelty detection. Applications of Machine Learning 2021. 118430I.PDF icon Spie2021.pdf (5.33 MB)
Franz, M. O., & Mallot H. A. (2000).  Biomimetic robot navigation. Robotics and Autonomous Systems. 30, 133 – 153.PDF icon Franz, Mallot_2000_Biomimetic robot navigation.pdf (171.77 KB)
Rohrbach, J., Rainhard T., Sick B., & Dürr O. (2019).  Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.

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