Biblio

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Dahmen, H.-J., Franz M. O., & Krapp H. G. (2001).  Extracting egomotion from optic flow: limits of accuracy and neural matched filters. (Zanker, J. M., & Zeil J., Ed.).{Motion Vision: Computational, Neural and Ecological Constraints}. 143-168.PDF icon Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)
Danhof, M., Schneider T., Laube P., & Umlauf G. (2015).  A Virtual-Reality 3d-Laser-Scan Simulation. BW-CAR| SINCOM. 68.
Denker, K., Hagel D., Raible J., Umlauf G., & Hamann B. (2013).  On-line reconstruction of CAD geometry. International Conference on 3d Vision. PDF icon OnlineReconstruction.pdf (392.38 KB)
Denker, K., Lehner B., & Umlauf G. (2008).  Online triangulation of laser-scan data. (Garimella, R., Ed.).Proceedings of the 17th International Meshing Roundtable 2008. PDF icon OnlineTriang.pdf (8.39 MB)
Denker, K., & Umlauf G. (2011).  Survey on benchmarks for a GPU based multi camera stereo matching algorithm. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop. PDF icon BenchmarkStereoMatching.pdf (3.63 MB)
Denker, K., Hamann B., & Umlauf G. (2015).  On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties. (Boissonnat, J-D., Cohen A., Gibaru O., Gout C., Lyche T., Mazure M-L., et al., Ed.).Curves and Surfaces, 8th International Conference, Paris 2014. 181-201.PDF icon OnlineCADReconst.pdf (3.18 MB)
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)
Denker, K., Lehner B., & Umlauf G. (2011).  Real-time triangulation of point streams. Engineering with Computers. 27, 67-80.PDF icon RTTriangulationPointStreams.pdf (1.05 MB)
Dieterich, W., Dürr O., Pendzig P., Bunde A., & Nitzan A. (1999).  Percolation concepts in solid state ionics. Physica A: Statistical Mechanics and its Applications. 266, 229–237.
Dieterich, W., Dürr O., Pendzig P., & Nitzan A. (1999).  Stochastic modelling of ion diffusion in complex systems. Anomalous Diffusion From Basics to Applications. 175–185.
Distler, H. K., van Veen H. A. H. C., Braun S. J., Heinz W., Franz M. O., & Bülthoff H. H. (1998).  Navigation in real and virtual environments: judging orientation and distance in a large-scale landscape. (Goebel, M., Lang U., Landauer J., & Walper M., Ed.).{Virtual Environment 98: Proc. of the Eurographics Workshop 1998}. 124 – 133.
Dold, D., Arpogaus M., & Dürr O. (2023).  Deep probabilistic modelling for energy forecasting. PDF icon Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)
Dürr, O., Murina E., Siegismund D., Tolkachev V., Steigele S., & Sick B. (2018).  Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes. Assay and drug development technologies. 16, 343–349.PDF icon adt.2018.859.pdf (711.06 KB)
Dürr, O., Dieterich W., & Nitzan A. (2002).  Diffusion in polymer electrolytes and the dynamic percolation model. Solid state ionics. 149, 125–130.
Dürr, O., & Brandenburg A. (2012).  Using Community Structure for Complex Network Layout. arXiv preprint arXiv:1207.6282.
Dürr, O., Dieterich W., & Nitzan A. (2001).  Charge Transport in Polymer Ion Conductors: a Monte Carlo Study. arXiv preprint cond-mat/0106197.
Dürr, O. (2003).  Theoretical Studies of Relaxation and Ionic Transport in Polymers.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.
Dürr, O., Uzdilli F., & Cieliebak M. (2014).  JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest.. SemEval@ COLING. 366–369.
Dürr, O., Pendzig P., Dieterich W., & Nitzan A. (2001).  Model studies of diffusion in glassy and polymer ion conductors. arXiv preprint cond-mat/0106196.
Dürr, O., Dieterich W., & Nitzan A. (2004).  Coupled ion and network dynamics in polymer electrolytes: Monte Carlo study of a lattice model. The Journal of chemical physics. 121, 12732–12739.
Dürr, O. (1998).  Monte-carlo-simulationen zu polymeren ionenleitern.
Dürr, O., Pauchard Y., Browarnik D., Axthelm R., & Loeser M. (2015).  Deep Learning on a Raspberry Pi for Real Time Face Recognition.. Eurographics (Posters). 11–12.
Dürr, O., Dieterich W., Maas P., & Nitzan A. (2002).  Effective medium theory of conduction in stretched polymer electrolytes. arXiv preprint cond-mat/0202165.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.

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