Biblio

Export 238 results:
Author Title Type [ Year(Asc)]
2006
Bobach, T., & Umlauf G. (2006).  Natural neighbor interpolation and order of continuity. (Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon NatNeighborInterp.pdf (1.47 MB)
Prautzsch, H., & Umlauf G. (2006).  Parametrizations for triangular G^k spline surfaces of low degree. ACM Transactions on Graphics. 24, 1281-1293.PDF icon GkSplineSurf.pdf (539.25 KB)
Lehner, B., Umlauf G., Hamann B., & Ustin S. (2006).  Topographic distance functions for interpolation of meteorological data. (Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon TopoDistFunc.pdf (2.27 MB)
Franz, M. O., & Schölkopf B. (2006).  A unifying view of Wiener and Volterra theory and polynomial kernel regression. Neural Computation. 18, 3097 – 3118.PDF icon Franz, Schölkopf_2006_A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression.pdf (165.97 KB)
2005
Umlauf, G. (2005).  Analysis and tuning of subdivision schemes. (Jüttler, B., Ed.).Proceedings of Spring Conference on Computer Graphics SCCG 2005. PDF icon ATSubSchemes.pdf (765.94 KB)
Kienzle, W., Bakır G. H., Franz M. O., & Schölkopf B. (2005).  Face detection – efficient and rank deficient. (Saul, L. K., Weiss Y., & Bottou L., Ed.).{Advances in Neural Information Processing Systems 17}. 673–680.PDF icon Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
Kim, K. I., Franz M. O., & Schölkopf B. (2005).  Iterative kernel principal component analysis for image modeling. IEEE Trans. PAMI. 27, 1351 – 1366.PDF icon Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2005).  Learning an interest operator from eye movements. {Proc. Workshop on Bioinspired Information Processing 2005}. PDF icon Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
Ginkel, I., Peters J., & Umlauf G. (2005).  On normals and control nets. (Martin, R., Bez H., & M. 233-239 S. pages =, Ed.).Mathematics of Surfaces XI. PDF icon NormalsControlNets.pdf (117.42 KB)
Heyse, S., Brodte A., Bruttger O., Duerr O., Freeman T., Jung T., et al. (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
Dürr, O., Dieterich W., & Nitzan A. (2004).  Coupled ion and network dynamics in polymer electrolytes: Monte Carlo study of a lattice model. The Journal of chemical physics. 121, 12732–12739.
Kienzle, W., Bakır G. H., & Franz M. O. (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.PDF icon Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
Franz, M. O., & Schölkopf B. (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.PDF icon Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
Franz, M. O., & Schölkopf B. (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}.
Franz, M. O., Chahl J. S., & Krapp H. G. (2004).  Insect-inspired estimation of egomotion.. Neural Computation. 16, 2245–60.
Kim, K. I., Franz M. O., & Schölkopf B. (2004).  Kernel Hebbian algorithm for single-frame super-resolution. {Statistical Learning in Computer Vision (SLCV 2004), ECCV 2004 Workshop, Prague}. 135–149.PDF icon Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
Sinz, F., & Franz M. O. (2004).  Learning depth. (Bülthoff, H. H., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 7. Tübinger Wahrnehmungskonferenz (TWK 2004)}. 68.PDF icon Sinz et al Learning depth 2004.pdf (197 KB)
Sinz, F., J Candela Q., Bakır G. H., Rasmussen C. E., & Franz M. O. (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.
Bakır, G. H., Gretton A., Franz M. O., & Schölkopf B. (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.
Franz, M. O., Kwon Y., Rasmussen C. E., & Schölkopf B. (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.PDF icon Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
Umlauf, G. (2004).  A technique for verifying the smoothness of subdivision schemes. (Lucian, M.L., & Neamtu M., Ed.).Geometric Modeling and Computing: Seattle 2003. PDF icon subSchemes.pdf (110.4 KB)
2003
Bakır, G. H., Ilg W., Franz M. O., & Giese M. (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.
Ilg, W., Bakır G. H., Franz M. O., & Giese M. A. (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.PDF icon Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Franz, M. O., & Chahl J. S. (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.
Ilg, W., Bakır G. H., Franz M. O., & Giese M. (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.

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