Biblio

Export 218 results:
Author Title [ Type(Desc)] Year
Conference Paper
Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  Center-surround filters emerge from optimizing predictivity in a free-viewing task. {Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
Schuldt, T., Döringshoff K., Stühler J., Kovalchuk E., Franz M. O., Gohlke M., et al. (2013).  A compact high-performance frequency reference for space applications. {29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}. PDF icon Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
Bobach, T., Bertram M., & Umlauf G. (2006).  Comparison of Voronoi based scatterd data interpolation schemes. (Villanueva, J.J., Ed.).Proceedings of the Internationl Conference on Visualization, Imaging and Image Processing. PDF icon VoronoiInterp.pdf (4.63 MB)
Bakır, G. H., Ilg W., Franz M. O., & Giese M. (2003).  Constraints measures and reproduction of style in robot imitation learning. (Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 70.
Ginkel, I., & Umlauf G. (2006).  Controlling a subdivision tuning method. (Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. PDF icon SubTuning.pdf (553.79 KB)
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.
Laube, P., Franz M. O., & Umlauf G. (2018).  Deep Learning Parametrization for B-Spline Curve Approximation. 2018 International Conference on 3D Vision (3DV). 691–699.PDF icon 0109.pdf (675.91 KB)
Sick, B., Hothorn T., & Dürr O. (2021).  Deep transformation models: Tackling complex regression problems with neural network based transformation models. Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
Hermann, M., Dold D., Umlauf G., & Dürr O. (2022).  DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency. 2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. PDF icon AHM2022_DeepDoubt.pdf (238.98 KB)
Hermann, M., Madrid N., & Seepold R. (2017).  Detection of variations in holter ECG recordings based on dynamic cluster analysis. International Conference on Intelligent Decision Technologies.
Bodai, T., Lembo V., Lembo V., Lee S-S., Ishizu M., & Franz M. (2023).  Development and application of a climate emulator. EGU23.
Bobach, T., Farin G., Hansford D., & Umlauf G. (2007).  Discrete harmonic functions from local coordinates. (Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII. PDF icon HarmonicFunc.pdf (835.96 KB)
Schall, M., Schambach M-P., & Franz M. O. (2019).  Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification. 15th IAPR International Conference on Document Analysis and Recognition. PDF icon Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
Kienzle, W., Bakır G. H., & Franz M. O. (2004).  Efficient approximations for support vector machines for object detection. (Rasmussen, C. E., Bülthoff H. H., & Giese M. A., Ed.).{Pattern Recognition, Proc. of the 26th DAGM Symposium}. 54–61.PDF icon Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
Middendorf, L., Mühlbauer F., Umlauf G., & Bobda C. (2007).  Embedded vertex shader in FPGA. (A. al., R. et, Ed.).Embedded System Design: Topics, Techniques and Trends.
Laube, P., Franz M. O., & Umlauf G. (2017).  Evaluation of features for SVM-based classification of geometric primitives in point clouds. Machine Vision Applications (MVA), 2017 Fifteenth IAPR International Conference on. 59–62.PDF icon paper.pdf (1.5 MB)
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.
Kienzle, W., Bakır G. H., Franz M. O., & Schölkopf B. (2005).  Face detection – efficient and rank deficient. (Saul, L. K., Weiss Y., & Bottou L., Ed.).{Advances in Neural Information Processing Systems 17}. 673–680.PDF icon Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
Hermann, M., Umlauf G., & Franz M. O. (2022).  Fast and memory-efficient independent component analysis using Lie group techniques. International Conference on Curves and Surfaces.
Schambach, M-P., von der Nüll S., & Schall M. (2019).  Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms. ICDAR-OST: The 2nd International Workshop on Open Services and Tools for Document Analysis. PDF icon Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms.pdf (776.45 KB)
Burkhart, D., Hamann B., & Umlauf G. (2011).  Finite element analysis for linear elastic solids based on subdivision schemes. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop. PDF icon FEALinearElasticSolids.pdf (2.35 MB)
Peters, J., & Umlauf G. (2000).  Gaussian and mean curvature of subdivision surfaces. (Cipolla, R., & Martin R., Ed.).The Mathematics of Surfaces IX. PDF icon SubCurvat.pdf (112.52 KB)
Lehner, B., Hamann B., & Umlauf G. (2010).  Generalized swap operation for tetrahedrizations. (Hagen, H., Ed.).Scientific Visualization: Advanced Concepts. PDF icon SwapTetrahed.pdf (333.85 KB)
Bobach, T., Constantiniu A., Steinmann P., & Umlauf G. (2010).  Geometric properties of the adaptice Delaunay tessellation. (Dæhlen, M., Floater M.S., Lyche T., Merrien J.-L., Morken K., & Schumaker L.L., Ed.).Mathematical Methods of Curves and Surfaces, Tondsberg 2008. PDF icon ADTProperties.pdf (335.14 KB)
Dürr, O., & Dieterich W. (2007).  Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations. Superionic Conductor Physics. 1, 77–80.

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