Prautzsch, H., & Umlauf G. (1999).  Improved triangular subdivision schemes. (Wolter, F.-E., & Patrikalakis N.M., Ed.).Proceedings of the CGI '98.

• BibTeX

TriSub.pdf (1.73 MB)

Bobach, T., Bertram M., & Umlauf G. (2006).  Issues and implementation of C^1 and C^2 natural neighbor interpolation. (G. al., B. et, Ed.).Advances in Visual Computing. Part II.

• BibTeX

C1C2NeighborInterp.pdf (3.87 MB)

Lehner, B., Umlauf G., & Hamann B. (2007).  Image Compression Using Data-Dependent Triangulations. (al., G. Bebis et, Ed.).Advances in Visual Computing.

• BibTeX

ImgCompression.pdf (3.75 MB)

Burkhart, D., Hamann B., & Umlauf G. (2010).  Iso-geometric analysis based on Catmull-Clark solid subdivision. Computer Graphics Forum. 29, 1575-1784.

• BibTeX

IsoCatmullClarkSub.pdf (3.69 MB)

Schall, M., Schambach M-P., & Franz M. O. (2016).  Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method. Conference: BW-CAR Symposium on Information and Communication Systems (SInCom).

• BibTeX

2016-12 Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method.pdf (803.54 KB)

Schall, M., Schambach M-P., & Franz M. O. (2016).  Increasing robustness of handwriting recognition using character n-gram decoding on large lexica. 12th IAPR International Workshop on Document Analysis Systems.

• BibTeX

Schall et al_2016_Increasing robustness of handwriting recognition using character n-gram decoding on large lexica.pdf (440.82 KB)

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).

• BibTeX

gcvc18.pdf (5.73 MB)

Herzog, L., Murina E., Dürr O., Wegener S., & Sick B. (2020).  Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis. 65, 101790.

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Hermann, M., Goldlücke B., & Franz M. O. (2022).  Image novelty detection based on mean-shift and typical set size. 21th International Conference on Image Analysis and Processing, ICIAP.

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ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)

Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2023).  Incremental one-class learning using regularized null-space training for industrial defect detection. 16th International Conference on Machine Vision (ICMV).

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Sick, B., & Dürr O. (2025).  Interpretable Neural Causal Models with TRAM-DAGs. (Huang, B., & Drton M., Ed.).Proceedings of the Fourth Conference on Causal Learning and Reasoning. 606–630.

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