Biblio

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Author [ Title(Desc)] Type Year
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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., Himpel B., & Vartziotis D. (2018).  GETOpt mesh smoothing: Putting GETMe in the framework of global optimization-based schemes. Finite Elem. Anal. Des.. 147,
Dürr, O., & Dieterich W. (2007).  Glassy and Polymeric Ionic Conductors: Statistical Modeling and Monte Carlo Simulations. Superionic Conductor Physics. 1, 77–80.
H
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)
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)
Franz, M. O., Schölkopf B., & Bülthoff H. H. (1997).  Homing by parameterized scene matching. {Proc. 4th Europ. Conf. on Artificial Life}. 236 – 245.
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)
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)
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)
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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)
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)
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)
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)
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.
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. (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}.
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.
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)
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)
Franzini, A., Dürr O., Baty F., Hosang J., & Brutsche M. H. (2014).  In Silico Identification of Cell-type-specific Compartmental Gene Expression Signatures with Predictive Value for Response to Erlotinib/bevacizumab Therapy in Non-small Cell Lung Cancer (nsclc). Respiration. 87, 562.
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)
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2023).  Incremental one-class learning using regularized null-space training for industrial defect detection. 16th International Conference on Machine Vision (ICMV).
Schwamberger, V., Le P. H. D., Schölkopf B., & Franz M. O. (2010).  The influence of the image basis on modeling and steganalysis performance. (Böhme, R., Fong P.W.L., & Safavi-Naini R., Ed.).{Proc. 12th Intl. Conf. on Information Hiding (IH-2010)}. 133–144.PDF icon Schwamberger et al._2010_The Influence of the Image Basis on Modeling and Steganalysis Performance.pdf (392.83 KB)
Franz, M. O., Chahl J. S., & Krapp H. G. (2004).  Insect-inspired estimation of egomotion.. Neural Computation. 16, 2245–60.
Franz, M. O., & Chahl J. S. (2002).  Insect-inspired estimation of self-motion. (Bülthoff, H. H., Lee S.-W., Poggio T. A., & Wallraven C., Ed.).{Proc. 2nd Workshop on Biologically Motivated Computer Vision (BMCV 2002)}. 2525, 171-180.PDF icon Franz, Chahl_2002_Insect-inspired estimation of self-motion.pdf (274.5 KB)

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