Biblio

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Zirkler, R., Eckhard T., & Jödicke B. (2018).  A concept for a multi-spectral snap-shot camera based on single-chip RGB sensor. Forum Bildverarbeitung. Poster.
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Yovel, Y., Melcón M. L., Franz M. O., Denzinger A., & Schnitzler H.-U. (2009).  The voice of bats: how greater mouse-eared bats recognize individuals based on their echolocation calls. PLoS Comput. Biol.. 5, e1000400:10.1371/journal.pcbi.1000400.PDF icon Yovel et al._2009_The voice of bats how greater mouse-eared bats recognize individuals based on their echolocation calls.pdf (458.53 KB)
Yovel, Y., Stilz P., Franz M. O., & Schnitzler H.-U. (2008).  The statistics of plant echoes as perceived by echolocating bats. {ASA Meeting Paris}.
Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2011).  Echo-based object recognition in echolocating bats. J. Comp. Phyiol. A. 197, 475–490.
Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2008).  Plant classification from bat-like echolocation signals. PLoS Comput.\ Biol.. 4, e1000032.PDF icon Yovel et al._2008_Plant classification from bat-like echolocation signals.pdf (772.54 KB)
Yovel, Y., Stilz P., Franz M. O., & Schnitzler H.-U. (2008).  The statistics of plant echoes as perceived by echolocating bats. {Proc. of the Computational and Systems Neuroscience Meeting 2008 (COSYNE 2008)}.
Yovel, Y., Franz M. O., Stilz P., & Schnitzler H.-U. (2008).  The statistics of plant echoes as perceived by echolocating bats. {Acoustics 08, Paris, SBN 978-2-9521105-4- 9 - EAN 9782952110549}. 123, DVD Proc..
Yovel, Y., Stilz P., Franz M. O., Boonman A., & Schnitzler H.-U. (2009).  What a plant sounds like: the statistics of vegetation echoes as received by echolocating bats. PLoS Comput. Biol.. 5, e1000429. doi:10.1371/journal.pcbi.1000429.PDF icon Yovel et al._2009_What a plant sounds like the statistics of vegetation echoes as received by echolocating bats.pdf (855.02 KB)
Yovel, Y., Stilz P., Melcón M. L., Franz M. O., & Schnitzler H.-U. (2008).  Bats can use echolocation calls for individual recognition. {Proc. Sensory coding and the natural environment 2008}.
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Umlauf, G. (2000).  Analyzing the characteristic map of triangular subdivision schemes. Constructive Approximation. 16, 145-155.PDF icon LoopCharMap.pdf (431.79 KB)
Umlauf, G. (2004).  A technique for verifying the smoothness of subdivision schemes. (Lucian, M.L., & Neamtu M., Ed.).Geometric Modeling and Computing: Seattle 2003. PDF icon subSchemes.pdf (110.4 KB)
Umlauf, G. (2005).  Analysis and tuning of subdivision schemes. (Jüttler, B., Ed.).Proceedings of Spring Conference on Computer Graphics SCCG 2005. PDF icon ATSubSchemes.pdf (765.94 KB)
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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)
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Staedtgen, M., Hahn S., Franz M. O., & Spitzer M. (2000).  Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen. (Bülthoff, H. H., Gegenfurtner K. R., & Mallot H. A., Ed.).{Proc. 3. Tübinger Wahrnehmungskonferenz (TWK 20009)}. 98.
Stadelmann, T., Glinski-Haefeli S., Gerber P., & Dürr O. (2018).  Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 333–345.PDF icon ANNPR_2018b.pdf (692.47 KB)
Stadelmann, T., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
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.
Sinz, F., & Franz M. O. (2004).  Learning depth. (Bülthoff, H. H., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 7. Tübinger Wahrnehmungskonferenz (TWK 2004)}. 68.PDF icon Sinz et al Learning depth 2004.pdf (197 KB)
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.
Siegismund, D., Tolkachev V., Heyse S., Sick B., Dürr O., & Steigele S. (2018).  Developing deep learning applications for life science and pharma industry. Drug research. 68, 305–310.
Sick, B., Hothorn T., & Dürr O. (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.
Seepold, R., Dermati C., Kostka A., Pfeil L., Lange R., Hermann M., et al. (2016).  Analyzing environmental conditions and vital signs to increase healthy living. Mobile Networks for Biometric Data Analysis.
Schwamberger, V., & Franz M. O. (2010).  Simple algorithmic modifications for improving blind steganalysis performance. {Proc. of the 2010 Workshop on Multimedia and Security (MM&Sec 2010)}. PDF icon Schwamberger, Franz_2010_Simple Algorithmic Modifications for Improving Blind Steganalysis Performance.pdf (813.87 KB)
Schwamberger, V., Le P. H. D., Schölkopf B., & Franz M. O. (2010).  The influence of the image basis on modeling and steganalysis performance. (Böhme, R., Fong P.W.L., & Safavi-Naini R., Ed.).{Proc. 12th Intl. Conf. on Information Hiding (IH-2010)}. 133–144.PDF icon Schwamberger et al._2010_The Influence of the Image Basis on Modeling and Steganalysis Performance.pdf (392.83 KB)
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)

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