Biblio
]
(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)
(2004). Efficient approximations for support vector machines for object detection.
{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.
{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.
{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.
{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.
{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 245 – 252.
(2004). Multivariate Regression via Stiefel Manifold Constraints.
{Pattern Recognition, Proc. of the 26th DAGM Symposium (DAGM 2004)}. 262-269.
(2004). Semi-supervised kernel regression using whitened function classes.
{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.
{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 70.
(2003). Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.
{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.
{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.
{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 74.
(2003). Robots with cognition?.
{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}.
(2002). Insect-inspired estimation of self-motion.
{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.
{SAB02 Workshop on Robotics as Theoretical Biology}.
(2001). Extracting egomotion from optic flow: limits of accuracy and neural matched filters.
{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)
(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.
{Proc. 3. Tübinger Wahrnehmungskonferenz (TWK 20009)}. 98.
(2000). Wide-field, motion-sensitive neurons and matched filters for optic flow fields.
Biol. Cybern.. 83, 185 – 197. Franz, Krapp_2000_Wide-field, motion-sensitive neurons and matched filters for optic flow fields.pdf (261.7 KB)
Franz, Krapp_2000_Wide-field, motion-sensitive neurons and matched filters for optic flow fields.pdf (261.7 KB)
(1999). Can fly tangential neurons be used to estimate self-motion?.
{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)
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.
{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)
Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)