Biblio

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K
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)}.
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)}.
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.PDF icon Kienzle, Franz, Schölkopf_2009_Center-surround patterns emerge as optimal predictors for human saccade targets.pdf (900.5 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.PDF icon Kienzle et al._2005_Face Detection --- Efficient and Rank Deficient.pdf (145.73 KB)
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.PDF icon Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
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.PDF icon Kienzle, Bakır, Franz_2004_Efficient approximations for support vector machines for object detection.pdf (165.13 KB)
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.PDF icon Kienzle et al._2007_A nonparametric approach to bottom-up visual saliency.pdf (879.52 KB)
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.
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}. PDF icon Kienzle et al._2006_Learning an Interest Operator from Human Eye Movements.pdf (1.41 MB)
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.PDF icon 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, W., Wichmann F. A., Schölkopf B., & Franz M. O. (2006).  Learning eye movements. {Proc. Sensory Coding and the Natural Environment 2006}.
Kim, K. I., Franz M. O., & Schölkopf B. (2005).  Iterative kernel principal component analysis for image modeling. IEEE Trans. PAMI. 27, 1351 – 1366.PDF icon Kim, Franz, Schölkopf_2005_Iterative Kernel Principal Component Analysis for Image Modeling.pdf (1.98 MB)
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.PDF icon Kim, Franz, Schölkopf_2004_Kernel Hebbian algorithm for single-frame super-resolution.pdf (2.22 MB)
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.
Kook, L., Baumann P. F. M., Dürr O., Sick B., & Rügamer D. (2024).  Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo. Journal of Statistical Software. 111,
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Deep and interpretable regression models for ordinal outcomes. arXiv preprint. 2010.08376.
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes. arXiv e-prints. 2010.08376.
L
Laube, P., Franz M. O., & Umlauf G. (2018).  Learnt knot placement in B-spline curve approximation using support vector machines. Computer Aided Geometric Design. 62, 104–116.PDF icon GMP18.pdf (865.85 KB)
Laube, P., & Umlauf G. (2016).  A short survey on recent methods for cage computation. BW-CAR| SINCOM. 37.PDF icon cagesurvSinCom.pdf (444.89 KB)
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.PDF icon 0109.pdf (675.91 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.PDF icon paper.pdf (1.5 MB)
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). PDF icon gcvc18.pdf (5.73 MB)
Le, P. H. D., & Franz M. O. (2010).  Single band statistics and steganalysis performance. {Proc. 6th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP-2010)}. 188–191.PDF icon Le, Franz_2010_Single Band Statistics and Steganalysis Performance.pdf (206.46 KB)
Le, P. H. D., Graf D., & Franz M. O. (2013).  Steganalysis in the presence of watermarked images. {Proc. 9th Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP- 2013)}. 513–517.PDF icon Le, Graf, Franz_2013_Steganalysis in the Presence of Watermarked Images.pdf (1.07 MB)
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.PDF icon Le, Franz_2012_How to find relevant training data A paired bootstrapping approach to blind steganalysis.pdf (476.58 KB)

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