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B

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

Bell, P, Schlecht, J and Ommer, B (2013). Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History. Visual Resources Journal, Special Issue on Digital Art History. Taylor & Francis. 29 26--37. http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111

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/1905.04132

PDF (8.02 MB)

C

Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer, Munich, Germany. 2191 269–276

Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276

Technical Report (324.55 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

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

E

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

Technical Report (398.88 KB)

Esser, P, Rombach, R and Ommer, B (2020). A Note on Data Biases in Generative Models. NeurIPS 2020 Workshop on Machine Learning for Creativity and Design. https://arxiv.org/abs/2012.02516

G

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. 231-288

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

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

H

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

Hering, F, Balschbach, G, Jähne, B and Waldhäusl, P (1996). A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves. Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing. 31 231--236. http://www.isprs.org/proceedings/XXXI/congress/part5/

J

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

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

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

Jähne, (1993). New trends in image processing hard- and software. Proceedings Image Analysis for Pulp and Paper Research and Production. 1--12

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

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

Jancsary, J, Nowozin, S and Rother, C (2012). Non-parametric crfs for image labeling. NIPS Workshop Modern Nonparametric Methods in Machine Learning. 1–5. http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf

Jehle, M and Jähne, B (2008). A novel method for three-dimensional three-component analysis of flow close to free water surfaces. Exp. Fluids. 44 469--480

Jehle, M and Jähne, B (2006). A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer. 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006

K

Kirschbaum, E (2019). Novel Machine Learning Approaches for Neurophysiological Data Analysis. Heidelberg University

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

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

L

Leue, C, Wenig, M, Platt, U, Jähne, B and Haußecker, H (2000). NOX Emissions Retrieved from Satellite Images. Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press. 654--655

M

Márquez-Neila, P, Kohli, P, Rother, C and Baumela, L (2014). Non-parametric higher-order random fields for image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8694 LNCS 269–284

Mbock, K (2009). A Novel Algorithm For Motion Estimation With Explicit Consideration Of Perturbations. University of Heidelberg

P

Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1998). Non-Rigid Image Registration Using a Parameter-Free Elastic Model. 9th British Machine Vision Conference (BMVC`98). Southampton/UK. 134–143

R

Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University

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