Biblio
Export 116 results:
Author Title Type [ Year
] Filters: First Letter Of Last Name is F [Clear All Filters]
(2006). A unifying view of Wiener and Volterra theory and polynomial kernel regression.
Neural Computation. 18, 3097 – 3118.
Franz, Schölkopf_2006_A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression.pdf (165.97 KB)
Franz, Schölkopf_2006_A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression.pdf (165.97 KB)
(2005). Face detection – efficient and rank deficient.
(Saul, L. K., Weiss Y., & Bottou L., Ed.).{Advances in Neural Information Processing Systems 17}. 673–680. Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
(2005). 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)
Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
(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)
(2005). Quantifying bioactivity on a large scale: quality assurance and analysis of multiparametric ultra-HTS data.
JALA: Journal of the Association for Laboratory Automation. 10, 207–212.
(2004). Efficient approximations for support vector machines for object detection.
(Rasmussen, C. E., Bülthoff H. H., & Giese M. A., Ed.).{Pattern Recognition, Proc. of the 26th DAGM Symposium}. 54–61. Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
(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)
(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}.
(2004). Insect-inspired estimation of egomotion..
Neural Computation. 16, 2245–60.
(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)
(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.
(2004). Multivariate Regression via Stiefel Manifold Constraints.
(Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc. of the 26th DAGM Symposium (DAGM 2004)}. 262-269.
(2004). Semi-supervised kernel regression using whitened function classes.
(Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 18 – 26. Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
(2003). Constraints measures and reproduction of style in robot imitation learning.
(Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 70.
(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)
(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.
(2003). A representation of complex movement sequences based on hierarchical spatio-temporal correspondence for imitation learning in robotics.
(Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 74.
(2003). Robots with cognition?.
(Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}.
(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)
(2002). Optimal linear estimation of self-motion - a real-world test of a model of fly tangential neurons.
(Prescott, T., & Webb B., Ed.).{SAB02 Workshop on Robotics as Theoretical Biology}.
(2001). Extracting egomotion from optic flow: limits of accuracy and neural matched filters.
(Zanker, J. M., & Zeil J., Ed.).{Motion Vision: Computational, Neural and Ecological Constraints}. 143-168. Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)
Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)
(2001). Melt viscosities of lattice polymers using a Kramers potential treatment.
J. Chem. Phys.. 115, 9042–9045.
(2000). Biomimetic robot navigation.
Robotics and Autonomous Systems. 30, 133 – 153. Franz, Mallot_2000_Biomimetic robot navigation.pdf (171.77 KB)
Franz, Mallot_2000_Biomimetic robot navigation.pdf (171.77 KB)
(2000). Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen.
(Bülthoff, H. H., Gegenfurtner K. R., & Mallot H. A., Ed.).{Proc. 3. Tübinger Wahrnehmungskonferenz (TWK 20009)}. 98.