Biblio
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(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)
(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). 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). 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). 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)
(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)
(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)
(1998). VS-neurons as matched filters for self-motion-induced optic flow fields.
(Elsner, N., & Wehner R., Ed.).{New Neuroethology on the Move}. II, 419.