Biblio
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(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)
(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.
(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)
(2007). Embedded vertex shader in FPGA.
(A. al., R. et, Ed.).Embedded System Design: Topics, Techniques and Trends.
(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)
(2019). Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.
15th IAPR International Conference on Document Analysis and Recognition. Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification.pdf (553.24 KB)
(2007). Discrete harmonic functions from local coordinates.
(Martin, R., Sabin M., & Winkler J., Ed.).Mathematics of Surfaces XII. HarmonicFunc.pdf (835.96 KB)
HarmonicFunc.pdf (835.96 KB)
(2023). Development and application of a climate emulator.
EGU23.
(2017). Detection of variations in holter ECG recordings based on dynamic cluster analysis.
International Conference on Intelligent Decision Technologies.
(2022). DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency.
2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. AHM2022_DeepDoubt.pdf (238.98 KB)
AHM2022_DeepDoubt.pdf (238.98 KB)
(2021). Deep transformation models: Tackling complex regression problems with neural network based transformation models.
Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
(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)
(2015). Deep Learning on a Raspberry Pi for Real Time Face Recognition..
Eurographics (Posters). 11–12.
(2006). Controlling a subdivision tuning method.
(Cohen, A., Merrien J.-L., & Schumaker L.L., Ed.).Curve and Surface Fitting. SubTuning.pdf (553.79 KB)
SubTuning.pdf (553.79 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.
(2006). Comparison of Voronoi based scatterd data interpolation schemes.
(Villanueva, J.J., Ed.).Proceedings of the Internationl Conference on Visualization, Imaging and Image Processing. VoronoiInterp.pdf (4.63 MB)
VoronoiInterp.pdf (4.63 MB)
(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)
(2007). Center-surround filters emerge from optimizing predictivity in a free-viewing task.
{Proc. of the Computational and Systems Neuroscience Meeting 2007 (COSYNE 2007)}.
(2018). Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering.
IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 333–345. ANNPR_2018b.pdf (692.47 KB)
ANNPR_2018b.pdf (692.47 KB)
(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)
(2021). Biologically-inspired vs. CNN texture representations in novelty detection.
Applications of Machine Learning 2021. 118430I. Spie2021.pdf (5.33 MB)
Spie2021.pdf (5.33 MB)
(2008). Bats can use echolocation calls for individual recognition.
{Proc. Sensory coding and the natural environment 2008}.
(2018). Automatic classification of non-small cell lung cancer histologic sub-types by deep learning.
VIRCHOWS ARCHIV. 108-108.
(2016). Analyzing environmental conditions and vital signs to increase healthy living.
Mobile Networks for Biometric Data Analysis.
(2005). Analysis and tuning of subdivision schemes.
(Jüttler, B., Ed.).Proceedings of Spring Conference on Computer Graphics SCCG 2005. ATSubSchemes.pdf (765.94 KB)
ATSubSchemes.pdf (765.94 KB)