Biblio

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1999
Prautzsch, H., & Umlauf G. (1999).  Triangular G^2 splines. (Laurent, P.-L., Sablonniere P., & Schumaker L.L., Ed.).Curve and Surface Design. PDF icon TriG2Splines.pdf (393.87 KB)
Prautzsch, H., & Umlauf G. (1999).  Triangular G^2 splines. (Laurent, P.-L., Sablonniere P., & Schumaker L.L., Ed.).Curve and Surface Design. PDF icon TriG2Splines.pdf (393.87 KB)
2006
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)
Ginkel, I., & Umlauf G. (2006).  Loop subdivision with curvature control. (Polthier, K., & Sheffer A., Ed.).Eurographics Symposium on Geometry Processing. PDF icon LoopSubCurv.pdf (6.03 MB)
2007
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)
Ginkel, I., & Umlauf G. (2007).  Tuning subdivision algorithms using constrained energy minimization. (Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII. PDF icon SubEnergyOpt.pdf (828.98 KB)
2008
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)
2010
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)
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)
2015
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)
Grunwald, M., Müller J., Schall M., Laube P., Umlauf G., & Franz M. O. (2015).  Pixel-wise Hybrid Image Registration on Wood Decors. BW-CAR| SINCOM. 24.PDF icon Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)
Schall, M., Grunwald M., Umlauf G., & Franz M. O. (2015).  Radiometric calibration of digital cameras using Gaussian processes. SPIE Optics+ Optoelectronics. PDF icon Schall et al_2015_Radiometric calibration of digital cameras using Gaussian processes.PDF (953.2 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)
Danhof, M., Schneider T., Laube P., & Umlauf G. (2015).  A Virtual-Reality 3d-Laser-Scan Simulation. BW-CAR| SINCOM. 68.
2016
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.
Schall, M., Schambach M-P., & Franz M. O. (2016).  Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method. Conference: BW-CAR Symposium on Information and Communication Systems (SInCom). PDF icon 2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
Schall, M., Schambach M-P., & Franz M. O. (2016).  Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method. Conference: BW-CAR Symposium on Information and Communication Systems (SInCom). PDF icon 2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
Schall, M., Schambach M-P., & Franz M. O. (2016).  Increasing robustness of handwriting recognition using character n-gram decoding on large lexica. 12th IAPR International Workshop on Document Analysis Systems. PDF icon Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)
Schall, M., Schambach M-P., & Franz M. O. (2016).  Increasing robustness of handwriting recognition using character n-gram decoding on large lexica. 12th IAPR International Workshop on Document Analysis Systems. PDF icon Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)
2017
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.
Grunwald, M., Laube P., Schall M., Umlauf G., & Franz M. O. (2017).  Radiometric calibration of digital cameras using neural networks. Optics and Photonics for Information Processing XI.
2018
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.
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.
Siegismund, D., Tolkachev V., Heyse S., Sick B., Dürr O., & Steigele S. (2018).  Developing deep learning applications for life science and pharma industry. Drug research. 68, 305–310.
Siegismund, D., Tolkachev V., Heyse S., Sick B., Dürr O., & Steigele S. (2018).  Developing deep learning applications for life science and pharma industry. Drug research. 68, 305–310.

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