Franz, M. O., Schölkopf B., Mallot H. A., & Bülthoff H. H. (1996).  Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten. (Thielscher, M., & Bornscheuer S.-E., Ed.).Fortschritte der {Künstlichen Intelligenz}. 92.

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Franz et al._1996_Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.pdf (63.1 KB)

Yovel, Y., Stilz P., Melcón M. L., Franz M. O., & Schnitzler H.-U. (2008).  Bats can use echolocation calls for individual recognition. {Proc. Sensory coding and the natural environment 2008}.

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Franz, M. O., & Mallot H. A. (2000).  Biomimetic robot navigation. Robotics and Autonomous Systems. 30, 133 – 153.

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Franz, Mallot_2000_Biomimetic robot navigation.pdf (171.77 KB)

Franz, M. O., Neumann T. R., Plagge M., Mallot H. A., & Zell A. (1999).  Can fly tangential neurons be used to estimate self-motion?. (Willshaw, D., & Murray A., Ed.).{Proc. of the 9th Intl. Conf. on Artificial Neural Networks (ICANN 1999)}. CP 470, 994-999.

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Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)

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)}.

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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.

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

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)}.

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

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.

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Laube, P., Franz M. O., & Umlauf G. (2018).  Deep Learning Parametrization for B-Spline Curve Approximation. 2018 International Conference on 3D Vision (3DV). 691–699.

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0109.pdf (675.91 KB)

Schuldt, T., Schubert C., Krutzik M., Bote L., Gaaloul N., Hartwig J., et al. (2015).  Design of a dual species atom interferometer for space. Experimental Astronomy. 39, 167-206.

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Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)

Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2011).  Echo-based object recognition in echolocating bats. J. Comp. Phyiol. A. 197, 475–490.

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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)

Laube, P., Franz M. O., & Umlauf G. (2017).  Evaluation of features for SVM-based classification of geometric primitives in point clouds. Machine Vision Applications (MVA), 2017 Fifteenth IAPR International Conference on. 59–62.

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paper.pdf (1.5 MB)

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)

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)

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)

Franz, M. O., Schölkopf B., & Bülthoff H. H. (1997).  Homing by parameterized scene matching. {Proc. 4th Europ. Conf. on Artificial Life}. 236 – 245.

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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.

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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)

Le, P. H. D., & Franz M. O. (2012).  How to find relevant training data: a paired bootstrapping approach to blind steganalysis. {4th IEEE Intl. Workshop on Information Forensics and Security (WIFS 2012)}. 228–233.

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Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)

Hensler, J., Denker K., Franz M. O., & Umlauf G. (2011).  Hybrid face recognition based on real-time multi-camera stereo-matching. (G. al., B. et, Ed.).Advances in Visual Computing, Proc. ISVC 2011, LNCS. 158–167.

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Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)

Laube, P., Michael G., Franz M. O., & Umlauf G. (2018).  Image Inpainting for High-Resolution Textures using CNN Texture Synthesis. Computer Graphics & Visual Computing (CGVC).

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gcvc18.pdf (5.73 MB)

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.

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Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)

Franz, M. O., & Schölkopf B. (2006).  Implicit Volterra and Wiener series for higher-order image analysis. {Advances in Data Analysis 30th Ann. Conf. German Classification Society}. 60.

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Barbero, A., Franz M. O., van Drongelen W., Dorronsoro J. R., Schölkopf B., & Grosse-Wentrup M. (2009).  Implicit Wiener series analysis of epileptic seizure recordings.. {Ann. Intl. Conf. of the IEEE Engineering in Medicine and Biology Society}. 5304–5307.

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Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 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}.

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