Biblio
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). 
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). 
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)
(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)
(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)
(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 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)
(2004). 
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)
(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)}.
(2003). 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). 
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}.
(2002). 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)
(2001). 
Biomimetic robot navigation.
Robotics and Autonomous Systems. 30, 133 – 153.
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.
(2000).
(2000). Can fly tangential neurons be used to estimate self-motion?.
(Willshaw, D., & Murray A., Ed.).{Proc. of the 9th Intl. Conf. on Artificial Neural Networks (ICANN 1999)}. CP 470, 994-999.
Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
(1999). 
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). On robots and flies: Modeling the visual orientation behavior of flies.
Robotics and Autonomous Systems. 29, 227–242.
Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)
(1999). 