Biblio
]
(1998). Minimalistic visual navigation.
Fortschr.-Ber. VDI Reihe 8.
(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.
Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)
Dahmen, Franz, Krapp_2001_Extracting egomotion from optic flow- limits of accuracy and neural matched filters.pdf (223.04 KB)
(2008). A robot system for biomimetic navigation - from snapshots to metric embeddings of view graphs.
(Yeap, A. W., & Jefferies M., Ed.).{Robotics and Cognitive Approaches to Spatial Mapping}. 38, 297–314.
(1996). Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.
(Thielscher, M., & Bornscheuer S.-E., Ed.).Fortschritte der {Künstlichen Intelligenz}. 92. Franz et al._1996_Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.pdf (63.1 KB)
Franz et al._1996_Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.pdf (63.1 KB)
(2008). Bats can use echolocation calls for individual recognition.
{Proc. Sensory coding and the natural environment 2008}.
(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. Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
Franz et al._1999_Can fly tangential neurons be used to estimate self-motion.pdf (170.74 KB)
(2007). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2013). A compact high-performance frequency reference for space applications.
{29th Intl. Symposium on Space Technology and Science (ISTS 2013), Nagoya (Japan)}. Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
Schuldt et al._2013_A Compact High-Performance Frequency Reference for Space Applications.pdf (369.85 KB)
(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.
(2018). Deep Learning Parametrization for B-Spline Curve Approximation.
2018 International Conference on 3D Vision (3DV). 691–699. 0109.pdf (675.91 KB)
0109.pdf (675.91 KB)
(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. Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
(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. paper.pdf (1.5 MB)
paper.pdf (1.5 MB)
(2005). Face detection – efficient and rank deficient.
(Saul, L. K., Weiss Y., & Bottou L., Ed.).{Advances in Neural Information Processing Systems 17}. 673–680. Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
(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. Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
(1997). Homing by parameterized scene matching.
{Proc. 4th Europ. Conf. on Artificial Life}. 236 – 245.
(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. 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)
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)
(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. Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)
(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. Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
(2018). Image Inpainting for High-Resolution Textures using CNN Texture Synthesis.
Computer Graphics & Visual Computing (CGVC). gcvc18.pdf (5.73 MB)
gcvc18.pdf (5.73 MB)
(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. Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
Franz, Schölkopf_2004_Implicit estimation of Wiener series.pdf (191.86 KB)
(2006). Implicit Volterra and Wiener series for higher-order image analysis.
{Advances in Data Analysis 30th Ann. Conf. German Classification Society}. 60.
(2009). Implicit Wiener series analysis of epileptic seizure recordings..
{Ann. Intl. Conf. of the IEEE Engineering in Medicine and Biology Society}. 5304–5307. Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
Barbero et al._2009_Implicit Wiener series analysis of epileptic seizure recordings.pdf (327.26 KB)
(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}.
(2007). Implicit Wiener series for estimating nonlinear receptive fields.
{Proc. 31st Göttingen Neurobiolgy Conf.}. 1199.
(2016). Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.
Conference: BW-CAR Symposium on Information and Communication Systems (SInCom). 2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)
2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)