Biblio
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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)
(2001). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Analyzing environmental conditions and vital signs to increase healthy living.
Mobile Networks for Biometric Data Analysis.
(2016). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2007). 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)
(2013). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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)
(2004). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model.
International Conference on Information Fusion (FUSION). 1-8.
(2023). 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)
(2005). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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)
(2003). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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)
(2007). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Kernel Hebbian algorithm for single-frame super-resolution.
{Statistical Learning in Computer Vision (SLCV 2004), ECCV 2004 Workshop, Prague}. 135–149.
Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
(2004). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Learning an interest operator from eye movements.
{Proc. Workshop on Bioinspired Information Processing 2005}.
Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
(2005). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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.
Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
(2006). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Learning eye movements.
{Proc. Sensory Coding and the Natural Environment 2006}.
(2006). 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.
(2007). 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.
NatNeighborInterp.pdf (1.47 MB)
(2006). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Nonlinear receptive field analysis: making kernel methods interpretable.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(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.
Kienzle et al._2007_A nonparametric approach to bottom-up visual saliency.pdf (879.52 KB)
(2007). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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.
Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
(2004). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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.
TopoDistFunc.pdf (2.27 MB)
(2006). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.
Visualization in Data Science (VDS at IEEE VIS) 2018.
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition.pdf (1.03 MB)
(2018). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
VS-neurons as matched filters for self-motion-induced optic flow fields.
(Elsner, N., & Wehner R., Ed.).{New Neuroethology on the Move}. II, 419.
(1998). Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms.
FIG Working Week 2020.
Fig2020.pdf (876.57 KB)
(2020). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Center-surround patterns emerge as optimal predictors for human saccade targets.
J. of Vision. 9, 1–15.
Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 KB)
(2009). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020).