Biblio

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3
Caputo, M., Denker K., Dums B., & Umlauf G. (2012).  3d hand gesture recognition based on sensor fusion of commodity hardware. (Reiterer, H., & Deussen O., Ed.).Mensch und Computer. PDF icon GestureRecognition.pdf (378.26 KB)
A
Stadelmann, T., Stockinger K., Braschler M., Cieliebak M., Baudinot G., Dürr O., et al. (2013).  Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit. Amsterdam, Netherlands.
B
Rohrbach, J., Rainhard T., Sick B., & Dürr O. (2019).  Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.
Rohrbach, J., Rainhard T., Sick B., & Dürr O. (2019).  Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.
D
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.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
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.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
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.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
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.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
E
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)
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (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.
L
Sinz, F., J Candela Q., Bakır G. H., Rasmussen C. E., & Franz M. O. (2004).  Learning depth from stereo. (Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 245 – 252.
Sinz, F., J Candela Q., Bakır G. H., Rasmussen C. E., & Franz M. O. (2004).  Learning depth from stereo. (Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc.\ 26th DAGM Symposium}. 3175, 245 – 252.
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Thießen, L., Laube P., Franz M. O., & Umlauf G. (2014).  Merging multiple 3d face reconstructions. (Benyoucef, D., & Reich C., Ed.).Symposium on Information and Communication Systems. 7-12.PDF icon Merging3DFaceReconst.pdf (12.91 MB)
Bakır, G. H., Gretton A., Franz M. O., & Schölkopf B. (2004).  Multivariate Regression via Stiefel Manifold Constraints. (Rasmussen, C. E., Bülthoff H. H., Giese M. A., & Schölkopf B., Ed.).{Pattern Recognition, Proc. of the 26th DAGM Symposium (DAGM 2004)}. 262-269.
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Denker, K., Hagel D., Raible J., Umlauf G., & Hamann B. (2013).  On-line reconstruction of CAD geometry. International Conference on 3d Vision. PDF icon OnlineReconstruction.pdf (392.38 KB)
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Heyse, S., Brodte A., Bruttger O., Duerr O., Freeman T., Jung T., et al. (2005).  Quantifying bioactivity on a large scale: quality assurance and analysis of multiparametric ultra-HTS data. JALA: Journal of the Association for Laboratory Automation. 10, 207–212.
S
Franz, M. O., Kwon Y., Rasmussen C. E., & Schölkopf B. (2004).  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.PDF icon Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
Franz, M. O., Kwon Y., Rasmussen C. E., & Schölkopf B. (2004).  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.PDF icon Franz et al._2004_Semi-supervised kernel regression using whitened function classes.pdf (198.7 KB)
Aguilera, D. N., Ahlers H., Battelier B., Bawamia A., Bertoldi A., Bondarescu R., et al. (2014).  STE-QUEST — Test of the universality of free fall using cold atom interferometry. Classical and Quantum Gravity. 31, 115010.PDF icon Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
Aguilera, D. N., Ahlers H., Battelier B., Bawamia A., Bertoldi A., Bondarescu R., et al. (2014).  STE-QUEST — Test of the universality of free fall using cold atom interferometry. Classical and Quantum Gravity. 31, 115010.PDF icon Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
Aguilera, D. N., Ahlers H., Battelier B., Bawamia A., Bertoldi A., Bondarescu R., et al. (2014).  STE-QUEST — Test of the universality of free fall using cold atom interferometry. Classical and Quantum Gravity. 31, 115010.PDF icon Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
Aguilera, D. N., Ahlers H., Battelier B., Bawamia A., Bertoldi A., Bondarescu R., et al. (2014).  STE-QUEST — Test of the universality of free fall using cold atom interferometry. Classical and Quantum Gravity. 31, 115010.PDF icon Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)