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.

• BibTeX

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

Seepold, R., Dermati C., Kostka A., Pfeil L., Lange R., Hermann M., et al. (2016).  Analyzing environmental conditions and vital signs to increase healthy living. Mobile Networks for Biometric Data Analysis.

• BibTeX

Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  Center-surround filters emerge from optimizing predictivity in a free-viewing task. {Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.

• BibTeX

Schuldt, T., Döringshoff K., Stühler J., Kovalchuk E., Franz M. O., Gohlke M., et al. (2013).  A compact high-performance frequency reference for space applications. {29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}.

• BibTeX

Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)

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.

• BibTeX

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

Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.

• BibTeX

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.

• BibTeX

Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 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.

• BibTeX

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

Kienzle, W., Schölkopf B., Wichmann F. A., & Franz M. O. (2007).  How to find interesting locations in video: a spatiotemporal interest point detector learned from human eye movements. {Lecture Notes in Computer Science: Pattern Recognition (DAGM 2007)}. 405–417.

• BibTeX

Kienzle et al._2007_How to find interesting locations in video a spatiotemporal interest point detector learned from human eye movements.pdf (377.26 KB)

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.

• BibTeX

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

• BibTeX

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 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.

• BibTeX

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}.

• BibTeX

Kienzle, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  Learning the influence of spatio-temporal variations in local image structure on visual saliency. (Bülthoff, H. H., Chatziastros A., Mallot H. A., & Ulrich R., Ed.).{Proc. 10. Tübinger Wahr\-neh\-mungs\-konferenz (TWK 2007)}. 63.

• BibTeX

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.

• BibTeX

NatNeighborInterp.pdf (1.47 MB)

Kienzle, W., Macke J. H., Wichmann F. A., Schölkopf B., & Franz M. O. (2007).  Nonlinear receptive field analysis: making kernel methods interpretable. {Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.

• BibTeX

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.

• BibTeX

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

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.

• BibTeX

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

• BibTeX

TopoDistFunc.pdf (2.27 MB)

Schall, M., Sacha D., Stein M., Franz M. O., & Keim D. A. (2018).  Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition. Visualization in Data Science (VDS at IEEE VIS) 2018.

• BibTeX

Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.pdf (1.03 MB)

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.

• BibTeX

Hake, F., Hermann M., Alkhatib H., Hesse C., Holste K., Umlauf G., et al. (2020).  Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms. FIG Working Week 2020.

• BibTeX

Fig2020.pdf (876.57 KB)

Kienzle, W., Franz M. O., & Schölkopf B. (2009).  Center-surround patterns emerge as optimal predictors for human saccade targets. J. of Vision. 9, 1–15.

• BibTeX

Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 KB)

Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.

• BibTeX

Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Deep and interpretable regression models for ordinal outcomes. arXiv preprint. 2010.08376.

• BibTeX