Biblio

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[ Author(Asc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
D
Dürr, O., & Dieterich W. (2007).  Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations. Superionic Conductor Physics. 1, 77–80.
Duerr, O., & Heyse S. (2006).  Multivariate analysis of siRNA/high content screening data.
Dold, D., Kobialka J., Palm N., Sommer E., Rügamer D., & Dürr O. (2025).  Paths and Ambient Spaces in Neural Loss Landscapes. (Li, Y., Mandt S., Agrawal S., & Khan E., Ed.).Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 10–18.
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)
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.
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.
Dieterich, W., Dürr O., Pendzig P., & Nitzan A. (2007).  Stochastic modelling of ion diffusion in complex systems. Anomalous Diffusion From Basics to Applications. 175–185.
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.
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.
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)
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., 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).  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)
Danhof, M., Schneider T., Laube P., & Umlauf G. (2015).  A Virtual-Reality 3d-Laser-Scan Simulation. BW-CAR| SINCOM. 68.
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)
C
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)
Cieliebak, M., Dürr O., & Uzdilli F. (2014).  Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.. Language Resources and Evaluation Conference (LREC). 3100–3104.
Cieliebak, M., Dürr O., & Uzdilli F. (2013).  Potential and Limitations of Commercial Sentiment Detection Tools.. ESSEM@ AI* IA. 47–58.
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.
Caputo, M., Denker K., Franz M. O., Laube P., & Umlauf G. (2014).  Learning geometric primitives in point clouds. Symposium on Geometry Processing, Cardiff 2014. PDF icon Caputo et al_2014_Learning geometric primitives in point clouds.pdf (630.12 KB)
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)
Caputo, M., Denker K., Franz M. O., Laube P., & Umlauf G. (2015).  Support Vector Machines for Classification of Geometric Primitives in Point Clouds. (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. 80-95.PDF icon Caputo et al_2015_Support vector machines for classification of geometric primitives in point clouds.pdf (2.64 MB)
B
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)

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