Biblio
]
(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. 
LaserScannerStereoMatching.pdf (584.47 KB)
LaserScannerStereoMatching.pdf (584.47 KB)
(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. Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
(1997). Homing by parameterized scene matching.
{Proc. 4th Europ. Conf. on Artificial Life}. 236 – 245.
(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. 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)
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)
(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. Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
(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. Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
(2007). Image Compression Using Data-Dependent Triangulations.
(al., G. Bebis et, Ed.).Advances in Visual Computing. ImgCompression.pdf (3.75 MB)
ImgCompression.pdf (3.75 MB)
(2018). Image Inpainting for High-Resolution Textures using CNN Texture Synthesis.
Computer Graphics & Visual Computing (CGVC). gcvc18.pdf (5.73 MB)
gcvc18.pdf (5.73 MB)
(2022). Image novelty detection based on mean-shift and typical set size.
21th International Conference on Image Analysis and Processing, ICIAP. ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
(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. Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
(2006). Implicit Volterra and Wiener series for higher-order image analysis.
{Advances in Data Analysis 30th Ann. Conf. German Classification Society}. 60.
(2009). Implicit Wiener series analysis of epileptic seizure recordings..
{Ann. Intl. Conf. of the IEEE Engineering in Medicine and Biology Society}. 5304–5307. Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
(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}.
(2007). Implicit Wiener series for estimating nonlinear receptive fields.
{Proc. 31st Göttingen Neurobiolgy Conf.}. 1199.
(1999). Improved triangular subdivision schemes.
(Wolter, F.-E., & Patrikalakis N.M., Ed.).Proceedings of the CGI '98. TriSub.pdf (1.73 MB)
TriSub.pdf (1.73 MB)
(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). 2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
(2016). Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.
12th IAPR International Workshop on Document Analysis Systems. Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)
Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)
(2023). Incremental one-class learning using regularized null-space training for industrial defect detection.
16th International Conference on Machine Vision (ICMV).
(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. Schwamberger et al._2010_The Influence of the Image Basis on Modeling and Steganalysis Performance.pdf (392.83 KB)
Schwamberger et al._2010_The Influence of the Image Basis on Modeling and Steganalysis Performance.pdf (392.83 KB)
(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. Franz, Chahl_2002_Insect-inspired estimation of self-motion.pdf (274.5 KB)
Franz, Chahl_2002_Insect-inspired estimation of self-motion.pdf (274.5 KB)
(2006). Issues and implementation of C^1 and C^2 natural neighbor interpolation.
(G. al., B. et, Ed.).Advances in Visual Computing. Part II. C1C2NeighborInterp.pdf (3.87 MB)
C1C2NeighborInterp.pdf (3.87 MB)
(2014). JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest..
SemEval@ COLING. 366–369.
(2004). 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)
Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
(2022). Large-scale independent component analysis by speeding up Lie group techniques.
International Conference on Acoustics, Speech, and Signal Processing, ICASSP. conference_101719.pdf (646.58 KB)
conference_101719.pdf (646.58 KB)
(2005). Learning an interest operator from eye movements.
{Proc. Workshop on Bioinspired Information Processing 2005}. Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)