Publications

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Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings, in press
N
Heiler, M and Schnörr, C (2005). Natural Image Statistics for Natural Image Segmentation. Int. J. Comp. Vision. 63 5–19
Heiler, M and Schnörr, C (2003). Natural Statistics for Natural Image Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV 2003). Nice, France. 1259-1266
Goldlücke, B, Strekalovskiy, E and Cremers, D (2012). The natural vectorial total variation which arises from geometric measure theory. SIAM Journal on Imaging Sciences
Goldlücke, B, Strekalovskiy, E and Cremers, D (2012). The Natural Vectorial Total Variation which Arises from Geometric Measure Theory. SIAM Journal on Imaging Sciences. 5 537-563
Trittler, S and Hamprecht, F A (2009). Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation. Digital Signal Processing. 19 628-639PDF icon Technical Report (511.23 KB)
Jähne, B, Jähne, B and Haußecker, H (2000). Neighborhood operators. Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press. 273--345
Jähne, B, Jähne, B and Haußecker, H (1999). Neighborhood operators. Handbook of Computer Vision and Applications. Volume II: Signal Processing and Pattern Recognition. Academic Press. 93--124
Jähne, (1987). Neue Ansätze zur Bildfolgenanalyse. Proc. 9. DAGM-Symposium zur Mustererkennung 1987. Springer. 149 287
Jähne, (2012). Neuerungen zum EMVA Standard 1288, Der Release 3.1 des etablierten Standards zur Kameracharakterisierung
Urban, G (2014). Neural Networks: Optimization And Applications. University of Heidelberg
Brachmann, E and Rother, C (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 4321–4330. http://arxiv.org/abs/1905.04132PDF icon PDF (8.02 MB)
Krasowki, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2017). Neuron Segmentation with High-Level Biological Priors. IEEE Transactions on Medical Imaging. 37
Singaraju, D, Rother, C and Rhemann, C (2010). New appearance models for natural image matting. 659–666
Singaraju, D, Rother, C and Rhemann, C (2009). New appearance models for natural image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 659–666
Voss, B, Heinlein, A, Jähne, B and Garbe, C S (2010). A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface. 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010
Eisele, H and Hamprecht, F A (2003). A new approach for defect detection in X-ray CT images. Pattern Recognition. Springer. 2449 345-352PDF icon Technical Report (398.88 KB)
Rother, C (2002). A new approach to vanishing point detection in architectural environments. Image and Vision Computing. 20 647–655
Scholz, T, Jähne, B, Suhr, H, Wehnert, G, Geißler, P and Schneider, K (1994). A new depth from focus technique for in situ determination of cell concentration in bioreactors. Proc. 16. DAGM-Symposium Mustererkennung. 145--150
Jähne, (1991). New experimental results on the parameters influencing air-sea gas exchange. Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota. ASCE. 582--592
Klinke, J and Jähne, B (1995). A new instrument for the optical measurement of the fine structure of the water surface in the field. IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves. 388
Balschbach, G, Menzel, M and Jähne, B (1995). A new instrument to measure steep wind-waves. IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves. 387
Jähne, B, Wais, T and Barabas, M (1984). A new optical bubble measuring device; a simple model for bubble contribution to gas exchange. Gas transfer at water surfaces. Reidel. 237--246
Richter, K E and Jähne, B (2009). New schemes for fast measurements of air-sea gas exchange in the Aeolotron lab. Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009
Jähne, (1993). New trends in image processing hard- and software. Proceedings Image Analysis for Pulp and Paper Research and Production. 1--12
Sprengel, R and Schnörr, C (1993). Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme. Mustererkennung 1993, 15. DAGM-Symposium. Springer Verlag. 134–141
Renard, B Y, Kirchner, M, Steen, H, Steen, J A J and Hamprecht, F A (2008). NITPICK: Peak Identification for Mass Spectrometry Data. BMC Bioinformatics. 9 355PDF icon Technical Report (643.89 KB)
Jähne, B and Schwarzbauer, M (2016). Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?. tm – Technisches Messen. 83 16–24
Zisler, M, Kappes, J H, Schnörr, C, Petra, S and Schnörr, C (2016). Non-Binary Discrete Tomography by Continuous Non-Convex Optimization. IEEE Comp. Imaging. 2 335-347
Garbe, C S, 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 (2008). Nonlinear Analysis of Multi-Dimensional Signals. Mathematical Methods in Signal Processing and Digital Image Analysis. Springer. 231-288PDF icon Technical Report (7.11 MB)
Garbe, C S, 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 (2008). 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. 231-288
Schnörr, C and Sprengel, R (1994). A Nonlinear Regularization Approach to Early Vision. Biol. Cybernetics. 72 141–149
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108PDF icon Technical Report (636.58 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision – ECCV 2002). Springer Verlag. 2351 93–108

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