Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  A nonparametric approach to bottom-up visual saliency. (Schölkopf, B., Platt J., & Hoffmann T., Ed.).{Advances in Neural Information Processing Systems 19}. 19, 689–696.

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Kienzle et al._2007_A nonparametric approach to bottom-up visual saliency.pdf (879.52 KB)

Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2006).  Learning an interest operator from human eye movements. (Schmid, C., Soatto S., & Tomasi C., Ed.).{Beyond Patches Workshop, Intl. Conf. on Computer Vision and Pattern Recognition}. 1–8.

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Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)

Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2006).  Learning eye movements. {Proc. Sensory Coding and the Natural Environment 2006}.

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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.

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NatNeighborInterp.pdf (1.47 MB)

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.

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TopoDistFunc.pdf (2.27 MB)

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.

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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.

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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}.

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Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)

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.

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Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)

Franz, M. O., Chahl J. S., & Krapp H. G. (2004).  Insect-inspired estimation of egomotion.. Neural Computation. 16, 2245–60.

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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.

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Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)

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.

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Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)

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.

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Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)

Dahmen, H.-J., Franz M. O., & Krapp H. G. (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.

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Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)

Franz, M. O., & Krapp H. G. (2000).  Wide-field, motion-sensitive neurons and matched filters for optic flow fields. Biol. Cybern.. 83, 185 – 197.

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Franz, Krapp_2000_Wide-field, motion-sensitive neurons and matched filters for optic flow fields.pdf (261.7 KB)

Franz, M. O., Hengstenberg R., & Krapp H. G. (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.

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