Biblio
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.
(2007). 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)
(2018). 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)
(2006). 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)
(2014). Learning eye movements.
{Proc. Sensory Coding and the Natural Environment 2006}.
(2006). 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). 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.
(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)
(2004). 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)
(2006). 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)
(2005). 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)
(2022). Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.
Assay and drug development technologies. 16, 343–349. adt.2018.859.pdf (711.06 KB)
(2018). 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). JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest..
SemEval@ COLING. 366–369.
(2014). 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). 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)
(2006). Iso-geometric analysis based on Catmull-Clark solid subdivision.
Computer Graphics Forum. 29, 1575-1784. IsoCatmullClarkSub.pdf (3.69 MB)
(2010). Integrating uncertainty in deep neural networks for MRI based stroke analysis.
Medical Image Analysis. 65, 101790.
(2020). 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)
(2002). Insect-inspired estimation of egomotion..
Neural Computation. 16, 2245–60.
(2004). 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)
(2010). Incremental one-class learning using regularized null-space training for industrial defect detection.
16th International Conference on Machine Vision (ICMV).
(2023). 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)
(2016). 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.
(2014). 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).