Biblio

Export 218 results:
Author [ Title(Asc)] 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 
I
Prautzsch, H., & Umlauf G. (1999).  Improved triangular subdivision schemes. (Wolter, F.-E., & Patrikalakis N.M., Ed.).Proceedings of the CGI '98. PDF icon TriSub.pdf (1.73 MB)
Franz, M. O., Macke J. H., Saleem A., & Schultz S. R. (2007).  Implicit Wiener series for estimating nonlinear receptive fields. {Proc. 31st Göttingen Neurobiolgy Conf.}. 1199.
Franz, M. O., & Schölkopf B. (2004).  Implicit Wiener series for capturing higher-order interactions in images. (Olshausen, B. A., & Lewicki M., Ed.).{Proc. Sensory Coding and the Natural Environment 2004}.
Barbero, A., Franz M. O., van Drongelen W., Dorronsoro J. R., Schölkopf B., & Grosse-Wentrup M. (2009).  Implicit Wiener series analysis of epileptic seizure recordings.. {Ann. Intl. Conf. of the IEEE Engineering in Medicine and Biology Society}. 5304–5307.PDF icon Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
Franz, M. O., & Schölkopf B. (2006).  Implicit Volterra and Wiener series for higher-order image analysis. {Advances in Data Analysis 30th Ann. Conf. German Classification Society}. 60.
Franz, M. O., & Schölkopf B. (2004).  Implicit estimation of Wiener series. (Barros, A., Principe J. C., Larsen J., Adali T., & Douglas S., Ed.).{Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop}. 735–744.PDF icon Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
Hermann, M., Goldlücke B., & Franz M. O. (2022).  Image novelty detection based on mean-shift and typical set size. 21th International Conference on Image Analysis and Processing, ICIAP. PDF icon ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
Laube, P., Michael G., Franz M. O., & Umlauf G. (2018).  Image Inpainting for High-Resolution Textures using CNN Texture Synthesis. Computer Graphics & Visual Computing (CGVC). PDF icon gcvc18.pdf (5.73 MB)
Lehner, B., Umlauf G., & Hamann B. (2007).  Image Compression Using Data-Dependent Triangulations. (al., G. Bebis et, Ed.).Advances in Visual Computing. PDF icon ImgCompression.pdf (3.75 MB)
H
Hensler, J., Denker K., Franz M. O., & Umlauf G. (2011).  Hybrid face recognition based on real-time multi-camera stereo-matching. (G. al., B. et, Ed.).Advances in Visual Computing, Proc. ISVC 2011, LNCS. 158–167.PDF icon Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
Le, P. H. D., & Franz M. O. (2012).  How to find relevant training data: a paired bootstrapping approach to blind steganalysis. {4th IEEE Intl. Workshop on Information Forensics and Security (WIFS 2012)}. 228–233.PDF icon Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
Kienzle, W., Schölkopf B., Wichmann F. A., & Franz M. O. (2007).  How to find interesting locations in video: a spatiotemporal interest point detector learned from human eye movements. {Lecture Notes in Computer Science: Pattern Recognition (DAGM 2007)}. 405–417.PDF icon Kienzle et al._2007_How to find interesting locations in video a spatiotemporal interest point detector learned from human eye movements.pdf (377.26 KB)
Franz, M. O., Schölkopf B., & Bülthoff H. H. (1997).  Homing by parameterized scene matching. {Proc. 4th Europ. Conf. on Artificial Life}. 236 – 245.
Ilg, W., Bakır G. H., Franz M. O., & Giese M. A. (2003).  Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning. (Nunes, U., de Almeida A., Bejczy A., Kosuge K., & Machado J., Ed.).{Proc. of the 11th International Conference on Advanced Robotics}. 2, 453–458.PDF icon Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Bender, C., Denker K., Friedrich M., Hirt K., & Umlauf G. (2012).  A hand-held laser scanner based on multi-camera stereo-matching. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop. PDF icon LaserScannerStereoMatching.pdf (584.47 KB)
G
Dürr, O., & Dieterich W. (2007).  Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations. Superionic Conductor Physics. 1, 77–80.
Bohnet, D., Himpel B., & Vartziotis D. (2018).  GETOpt mesh smoothing: Putting GETMe in the framework of global optimization-based schemes. Finite Elem. Anal. Des.. 147,
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)
Bohnet, D., & Vartziotis D. (2017).  A geometric mesh smoothing algorithm related to damped oscillations. Comput Methods Appl Mech Eng. 326C,
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)
Franzini, A., Baty F., Macovei I. I., Dürr O., Droege C., Betticher D., et al. (2015).  Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial). Clinical Cancer Research. clincanres––3135.
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)
Prautzsch, H., & Umlauf G. (1998).  A G^2 subdivision algorithm. Computing. 13, 217-224.PDF icon g2algorithm.pdf (196.14 KB)
Prautzsch, H., & Umlauf G. (2000).  A G^1 and a G^2 subdivision scheme for trinagular nets. International Journal on Shape Modelling. 6, 21-35.PDF icon G1G2TriAlgo.pdf (2.61 MB)

Pages