Biblio
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.
(1999). 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. Mallot et al._1997_The view-graph approach to visual navigation and spatial memory.pdf (212.16 KB)
(1997). Speaker Identification and Clustering using Convolution Neural Networks.
IEEE International workshop on Machine Learning for Signal Processing.
(2016). Learning embeddings for speaker clustering based on voice equality.
Machine Learning for Signal Processing (MLSP), 2017 IEEE 27th International Workshop on. 1–6. MLSP_2017.pdf (1.34 MB)
(2017). Image Compression Using Data-Dependent Triangulations.
(al., G. Bebis et, Ed.).Advances in Visual Computing. ImgCompression.pdf (3.75 MB)
(2007). Video compression using data-dependent triangulations.
(Xiao, Y., & E. Thij ten., Ed.).Computer Graphics and Visualization '08. VideoComprTriang.pdf (177.45 KB)
(2008). Generalized swap operation for tetrahedrizations.
(Hagen, H., Ed.).Scientific Visualization: Advanced Concepts. SwapTetrahed.pdf (333.85 KB)
(2010). 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. TriangColorImg.pdf (3.64 MB)
(2007). 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. TopoDistFunc.pdf (2.27 MB)
(2006). Single band statistics and steganalysis performance.
{Proc. 6th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP-2010)}. 188–191. Le, Franz_2010_Single Band Statistics and Steganalysis Performance.pdf (206.46 KB)
(2010). 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. Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
(2012). Steganalysis in the presence of watermarked images.
{Proc. 9th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP- 2013)}. 513–517. Le, Graf, Franz_2013_Steganalysis in the Presence of Watermarked Images.pdf (1.07 MB)
(2013). Learnt knot placement in B-spline curve approximation using support vector machines.
Computer Aided Geometric Design. 62, 104–116. GMP18.pdf (865.85 KB)
(2018). A short survey on recent methods for cage computation.
BW-CAR| SINCOM. 37. cagesurvSinCom.pdf (444.89 KB)
(2016). Deep Learning Parametrization for B-Spline Curve Approximation.
2018 International Conference on 3D Vision (3DV). 691–699. 0109.pdf (675.91 KB)
(2018). 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. paper.pdf (1.5 MB)
(2017). Image Inpainting for High-Resolution Textures using CNN Texture Synthesis.
Computer Graphics & Visual Computing (CGVC). gcvc18.pdf (5.73 MB)
(2018). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020). Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes.
arXiv e-prints. 2010.08376.
(2020). Iterative kernel principal component analysis for image modeling.
IEEE Trans. PAMI. 27, 1351 – 1366. Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
(2005). Kernel Hebbian algorithm for single-frame super-resolution.
{Statistical Learning in Computer Vision (SLCV 2004), ECCV 2004 Workshop, Prague}. 135–149. Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
(2004). Nonlinear receptive field analysis: making kernel methods interpretable.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2007). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2007).
(2009).