Biblio

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[ Author(Desc)] Title Type Year
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L
Laube, P., Franz M. O., & Umlauf G. (2018).  Learnt knot placement in B-spline curve approximation using support vector machines. Computer Aided Geometric Design. 62, 104–116.PDF icon GMP18.pdf (865.85 KB)
Laube, P., & Umlauf G. (2016).  A short survey on recent methods for cage computation. BW-CAR| SINCOM. 37.PDF icon cagesurvSinCom.pdf (444.89 KB)
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)
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)
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)
Le, P. H. D., & Franz M. O. (2010).  Single band statistics and steganalysis performance. {Proc. 6th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP-2010)}. 188–191.PDF icon Le, Franz_2010_Single Band Statistics and Steganalysis Performance.pdf (206.46 KB)
Le, P. H. D., Graf D., & Franz M. O. (2013).  Steganalysis in the presence of watermarked images. {Proc. 9th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP- 2013)}. 513–517.PDF icon Le, Graf, Franz_2013_Steganalysis in the Presence of Watermarked Images.pdf (1.07 MB)
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)
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)
Lehner, B., Umlauf G., & Hamann B. (2008).  Video compression using data-dependent triangulations. (Xiao, Y., & E. Thij ten., Ed.).Computer Graphics and Visualization '08. PDF icon VideoComprTriang.pdf (177.45 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)
Lehner, B., Umlauf G., & Hamann B. (2007).  Survey of techniques for data-dependent triangulations. (Hagen, H., Hering-Bertram M., & Garth C., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon TriangColorImg.pdf (3.64 MB)
Lehner, B., Umlauf G., Hamann B., & Ustin S. (2006).  Topographic distance functions for interpolation of meteorological data. (Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon TopoDistFunc.pdf (2.27 MB)
Lukic, Y., Vogt C., Dürr O., & Stadelmann T. (2016).  Speaker Identification and Clustering using Convolution Neural Networks. IEEE International workshop on Machine Learning for Signal Processing.
Lukic, Y. X., Vogt C., Dürr O., & Stadelmann T. (2017).  Learning embeddings for speaker clustering based on voice equality. Machine Learning for Signal Processing (MLSP), 2017 IEEE 27th International Workshop on. 1–6.PDF icon MLSP_2017.pdf (1.34 MB)
M
Mallot, H. A., Gillner S., Steck S. D., & Franz M. O. (1999).  Recognition-triggered response and the view-graph approach to spatial cognition. (Freksa, C., & Mark D. M., Ed.).{Spatial Information Theory - Cognitive and Computational Foundations of Geographic Information Science (COSIT 99)}. 1661, 367-380.
Mallot, H. A., Franz M. O., Schölkopf B., & Bülthoff H. H. (1997).  The view-graph approach to visual navigation and spatial memory. (Gerstner, W., Germond A., Hasler M., & Nicoud J.-D., Ed.).{Proc. of the 7th Intl. Conf. on Artificial Neural Networks (ICANN 97)}. 1327, 751 – 756.PDF icon Mallot et al._1997_The view-graph approach to visual navigation and spatial memory.pdf (212.16 KB)
McAuley, J. J., Caetano T. S., Smola A. J., & Franz M. O. (2006).  Learning high-order MRF priors of color images. {Proc. of the 23rd Intl. Conf. on Machine Learning (ICML 2006)}. 617–624.PDF icon McAuley et al._2006_Learning high-order MRF priors of color images.pdf (981.67 KB)
Meier, B. Bruno, Elezi I., Amirian M., Dürr O., & Stadelmann T. (2018).  Learning Neural Models for End-to-End Clustering. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 126–138.PDF icon ANNPR_2018a.pdf (3.43 MB)
Meier, B. Bruno, Stadelmann T., & Dürr O. (2018).  Learning to Cluster. PDF icon learning_to_cluster.pdf (1.82 MB)
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.

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