Biblio
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(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)
(2025). Interpretable Neural Causal Models with TRAM-DAGs.
(Huang, B., & Drton M., Ed.).Proceedings of the Fourth Conference on Causal Learning and Reasoning. 606–630.
(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)
(2006). Learning an interest operator from human eye movements.
(Schmid, C., Soatto S., & Tomasi C., Ed.).{Beyond Patches Workshop, Intl. Conf. on Computer Vision and Pattern Recognition}. 1–8. 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)
(2004). Learning depth.
(Bülthoff, H. H., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 7. Tübinger Wahrnehmungskonferenz (TWK 2004)}. 68. Sinz et al Learning depth 2004.pdf (197 KB)
Sinz et al Learning depth 2004.pdf (197 KB)
(2004). Learning depth from stereo.
(Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 245 – 252.
(2017). 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)
MLSP_2017.pdf (1.34 MB)
(2006). Learning eye movements.
{Proc. Sensory Coding and the Natural Environment 2006}.
(2014). Learning geometric primitives in point clouds.
Symposium on Geometry Processing, Cardiff 2014. Caputo et al_2014_Learning geometric primitives in point clouds.pdf (630.12 KB)
Caputo et al_2014_Learning geometric primitives in point clouds.pdf (630.12 KB)
(2006). Learning high-order MRF priors of color images.
{Proc. of the 23rd Intl. Conf. on Machine Learning (ICML 2006)}. 617–624. McAuley et al._2006_Learning high-order MRF priors of color images.pdf (981.67 KB)
McAuley et al._2006_Learning high-order MRF priors of color images.pdf (981.67 KB)
(2018). Learning Neural Models for End-to-End Clustering.
IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 126–138. ANNPR_2018a.pdf (3.43 MB)
ANNPR_2018a.pdf (3.43 MB)
(2007). Learning the influence of spatio-temporal variations in local image structure on visual saliency.
(Bülthoff, H. H., Chatziastros A., Mallot H. A., & Ulrich R., Ed.).{Proc. 10. Tübinger Wahr\-neh\-mungs\-konferenz (TWK 2007)}. 63.
(1997). Learning view graphs for robot navigation.
(Johnson, W. L., Ed.).{Proc.\ 1.İntl.\ Conf.\ on Autonomous Agents}. 138 – 147. Franz et al._1998_Learning View Graphs for Robot Navigation.pdf (1.26 MB)
Franz et al._1998_Learning View Graphs for Robot Navigation.pdf (1.26 MB)
(2003). Linear combinations of optic flow vectors for estimating self-motion-a real-world test of a neural model.
(Becker, S., Obermayer K., & Thrun S., Ed.).{Advances in Neural Information Processing Systems 15}. 1343–1350.
(2006). Loop subdivision with curvature control.
(Polthier, K., & Sheffer A., Ed.).Eurographics Symposium on Geometry Processing. LoopSubCurv.pdf (6.03 MB)
LoopSubCurv.pdf (6.03 MB)
(2018). LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries.
13th IAPR International Workshop on Document Analysis Systems. 2018-04 LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries.pdf (145.78 KB)
2018-04 LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries.pdf (145.78 KB)
(2014). Merging multiple 3d face reconstructions.
(Benyoucef, D., & Reich C., Ed.).Symposium on Information and Communication Systems. 7-12. Merging3DFaceReconst.pdf (12.91 MB)
Merging3DFaceReconst.pdf (12.91 MB)
(2014). Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools..
Language Resources and Evaluation Conference (LREC). 3100–3104.
(2018). Multi-Dimensional Connectionist Classification: Reading Text in One Step.
13th IAPR International Workshop on Document Analysis Systems. 2018-04 Multi-Dimensional Connectionist Classification Reading Text in One Step.pdf (485.57 KB)
2018-04 Multi-Dimensional Connectionist Classification Reading Text in One Step.pdf (485.57 KB)