Biblio

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2007
Lehner, B., Umlauf G., & Hamann B. (2007).  Survey of techniques for data-dependent triangulations. (Hagen, H., Hering-Bertram M., & Garth C., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon TriangColorImg.pdf (3.64 MB)
Ginkel, I., & Umlauf G. (2007).  Tuning subdivision algorithms using constrained energy minimization. (Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII. PDF icon SubEnergyOpt.pdf (828.98 KB)
2006
Ginkel, I., & Umlauf G. (2006).  Controlling a subdivision tuning method. (Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. PDF icon SubTuning.pdf (553.79 KB)
Ginkel, I., & Umlauf G. (2006).  Loop subdivision with curvature control. (Polthier, K., & Sheffer A., Ed.).Eurographics Symposium on Geometry Processing. PDF icon LoopSubCurv.pdf (6.03 MB)
2005
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)
2004
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)
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.
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)
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.
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)
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.
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.
Franz, M. O. (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)}.
2000
Staedtgen, M., Hahn S., Franz M. O., & Spitzer M. (2000).  Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen. (Bülthoff, H. H., Gegenfurtner K. R., & Mallot H. A., Ed.).{Proc. 3. Tübinger Wahrnehmungskonferenz (TWK 20009)}. 98.
1999
Mallot, H. A., Gillner S., Steck S. D., & Franz M. O. (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.
1998
Distler, H. K., van Veen H. A. H. C., Braun S. J., Heinz W., Franz M. O., & Bülthoff H. H. (1998).  Navigation in real and virtual environments: judging orientation and distance in a large-scale landscape. (Goebel, M., Lang U., Landauer J., & Walper M., Ed.).{Virtual Environment 98: Proc. of the Eurographics Workshop 1998}. 124 – 133.
1997
Franz, M. O., Schölkopf B., Georg P., Mallot H. A., & Bülthoff H. H. (1997).  Learning view graphs for robot navigation. (Johnson, W. L., Ed.).{Proc.\ 1.İntl.\ Conf.\ on Autonomous Agents}. 138 – 147.PDF icon Franz et al._1998_Learning View Graphs for Robot Navigation.pdf (1.26 MB)
Mallot, H. A., Franz M. O., Schölkopf B., & Bülthoff H. H. (1997).  The view-graph approach to visual navigation and spatial memory. (Gerstner, W., Germond A., Hasler M., & Nicoud J.-D., Ed.).{Proc. of the 7th Intl. Conf. on Artificial Neural Networks (ICANN 97)}. 1327, 751 – 756.PDF icon Mallot et al._1997_The view-graph approach to visual navigation and spatial memory.pdf (212.16 KB)
Mallot, H. A., Franz M. O., Schölkopf B., & Bülthoff H. H. (1997).  The view-graph approach to visual navigation and spatial memory. (Gerstner, W., Germond A., Hasler M., & Nicoud J.-D., Ed.).{Proc. of the 7th Intl. Conf. on Artificial Neural Networks (ICANN 97)}. 1327, 751 – 756.PDF icon Mallot et al._1997_The view-graph approach to visual navigation and spatial memory.pdf (212.16 KB)

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