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M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.

M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.

B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The Natural Vectorial Total Variation which Arises from Geometric Measure Theory”, SIAM Journal on Imaging Sciences, vol. 5, pp. 537-563, 2012.

B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The natural vectorial total variation which arises from geometric measure theory”, SIAM Journal on Imaging Sciences, 2012.

S. Trittler and Hamprecht, F. A., “Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation”, Digital Signal Processing, vol. 19, pp. 628-639, 2009.

Technical Report (511.23 KB)

B. Jähne, Jähne, B., and Haußecker, H., “Neighborhood operators”, Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press, p. 273--345, 2000.

B. Jähne, Jähne, B., and Haußecker, H., “Neighborhood operators”, Handbook of Computer Vision and Applications. Volume II: Signal Processing and Pattern Recognition. Academic Press, p. 93--124, 1999.

R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 2020.

R. Rombach, Esser, P., and Ommer, B., “Network-to-Network Translation with Conditional Invertible Neural Networks”, Neural Information Processing Systems (NeurIPS) (Oral). 2020.

B. Jähne, “Neue Ansätze zur Bildfolgenanalyse”, in Proc. 9. DAGM-Symposium zur Mustererkennung 1987, 1987, vol. 149, p. 287.

B. Jähne, “Neuerungen zum EMVA Standard 1288, Der Release 3.1 des etablierten Standards zur Kameracharakterisierung”. 2012.

G. Urban, “Neural Networks: Optimization and Applications”, University of Heidelberg, 2014.

E. Brachmann and Rother, C., “Neural-guided RANSAC: Learning where to sample model hypotheses”, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.

PDF (8.02 MB)

N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.

D. Singaraju, Rother, C., and Rhemann, C., “New appearance models for natural image matting”, 2010, pp. 659–666.

D. Singaraju, Rother, C., and Rhemann, C., “New appearance models for natural image matting”, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 659–666.

B. Voss, Heinlein, A., Jähne, B., and Garbe, C. S., “A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface”, in 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010, 2010.

H. Eisele and Hamprecht, F. A., “A new approach for defect detection in X-ray CT images”, Pattern Recognition, vol. 2449. Springer, pp. 345-352, 2003.

Technical Report (398.88 KB)

C. Rother, “A new approach to vanishing point detection in architectural environments”, in Image and Vision Computing, 2002, vol. 20, pp. 647–655.

T. Scholz, Jähne, B., Suhr, H., Wehnert, G., Geißler, P., and Schneider, K., “A new depth from focus technique for in situ determination of cell concentration in bioreactors”, in Proc. 16. DAGM-Symposium Mustererkennung, 1994, p. 145--150.

B. Jähne, “New experimental results on the parameters influencing air-sea gas exchange”, in Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota, 1991, p. 582--592.

J. Klinke and Jähne, B., “A new instrument for the optical measurement of the fine structure of the water surface in the field”, in IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves, 1995, p. 388.

G. Balschbach, Menzel, M., and Jähne, B., “A new instrument to measure steep wind-waves”, in IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves, 1995, p. 387.

B. Jähne, Wais, T., and Barabas, M., “A new optical bubble measuring device; a simple model for bubble contribution to gas exchange”, in Gas transfer at water surfaces, 1984, p. 237--246.

K. E. Richter and Jähne, B., “New schemes for fast measurements of air-sea gas exchange in the Aeolotron lab”, in Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009, 2009.

B. Jähne, “New trends in image processing hard- and software”, in Proceedings Image Analysis for Pulp and Paper Research and Production, 1993, p. 1--12.

R. Sprengel and Schnörr, C., “Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme”, in Mustererkennung 1993, 15. DAGM-Symposium, 1993, pp. 134–141.

B. Y. Renard, Kirchner, M., Steen, H., Steen, J. A. J., and Hamprecht, F. A., “NITPICK: Peak Identification for Mass Spectrometry Data”, BMC Bioinformatics, vol. 9, p. 355, 2008.

Technical Report (643.89 KB)

B. Jähne and Schwarzbauer, M., “Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?”, tm – Technisches Messen, vol. 83, pp. 16–24, 2016.

M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.

C. S. Garbe, Krajsek, K., Pavlov, P., Andres, B., Mühlich, M., Stuke, I., Mota, C., Böhme, M., Haker, M., Schuchert, T., Scharr, H., Aach, T., and Barth, E., “Nonlinear Analysis of Multi-Dimensional Signals”, Mathematical Methods in Signal Processing and Digital Image Analysis. Springer, pp. 231-288, 2008.

Technical Report (7.11 MB)

C. S. Garbe, Krajsek, K., Pavlov, P., Andres, B., Mühlich, M., Stuke, I., Mota, C., Böhme, M., Haker, M., Schucher, T., Scharr, H., Aach, T., and Barth, E., “Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns”, Mathematical Methods in Time Series Analysis and Digital Image Processing. Springer, pp. 231-288, 2008.

C. Schnörr and Sprengel, R., “A Nonlinear Regularization Approach to Early Vision”, Biol. Cybernetics, vol. 72, pp. 141–149, 1994.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

Technical Report (636.58 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.

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