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G. Balschbach, “Untersuchungen statistischer und geometrischer Eigenschaften von Windwellen und ihrer Wechselwirkung mit der wasserseitigen Grenzschicht”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2000.
G. Balschbach, “Verschiedene Verfahren zur Visualisierung und Größenbestimmung von Gasblasen in Wasser”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1994.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for moving specular surfaces”, in ECCV 1998, 1998, vol. 1407, p. 170--184.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for reconstruction of specular surfaces”, in Tagungsband Herbsttagung des Graduiertenkollegs "3D Bildanalyse und -synthese", 1997.
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. Balluff, Hanselmann, M., and Heeren, R. M. A., “Mass spectrometry imaging for the investigation of intratumor heterogeneity”, in Advances in Cancer Research, vol. 134, Elsevier, 2017, pp. 201-230.
L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.
A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.
A. Bailoni, “Deep Learning for Graph-Based Image Instance Segmentation”. Heidelberg University, 2021.
C. Bähnisch, Stelldinger, P., and Köthe, U., “Fast and Accurate 3D Edge Detection for Surface Reconstruction”, in Pattern Recognition, 2009, vol. 5748, pp. 111-120.
A
J. M. Álvarez, Gevers, T., Diego, F., and López, A. M., “Road Geometry Classification by Adaptive Shape Models”, IEEE Transactions on Intelligent Transportation Systems (ITS), vol. 99, pp. 1-10, 2012.
M. Atif, “Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2013.
M. Atif and Jähne, B., “Optimal Depth Estimation from a Single Image by Computational Imaging using Chromatic Aberrations”, in Forum Bildverarbeitung, 2012, p. 23--34.
M. Atif and Jähne, B., “Optimal Depth Estimation from a Single Image by Computational Imaging Using Chromatic Aberrations”, tm --- Technisches Messen, vol. 80, p. 343--348, 2013.
M. Atif, Zimmermann, K., and Jähne, B., “A space-variant (3D) image simulation tool for computational cameras”, in International Conference on Computational Photography (ICCP) 2010, 2010.
M. Atif, “Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations”, vol. Dissertation. IWR, Univ. Heidelberg, 2013.
F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.
F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”, J. Math. Imag. Vision, vol. 58, pp. 211–238, 2017.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.
F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”. 2016.
N. Arnold, “Visualisierung des Gasaustauschs an der windbewegten Wasseroberfläche mittels vertikaler Konzentrationsfelder von gelöstem Sauerstoff quer zur Windrichtung”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2015.
M. Arnold, Bell, P., and Ommer, B., “Automated Learning of Self-Similarity and Informative Structures in Architecture”, in Scientific Computing & Cultural Heritage, 2013.
A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.
H. Arlt, Sui, X., Folger, B., Adams, C., Chen, X., Remme, R., Hamprecht, F. A., DiMaio, F., Liao, M., Goodman, J. M., Farese, R. V., and Walther, T. C., “Seipin forms a flexible cage at lipid droplet formation sites”. bioRxiv, 2021.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Mackowiak, R., Rother, C., and Köthe, U., “Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”, 2020.PDF icon PDF (2.87 MB)
B. Antic, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery”, in Medical Image Computing and Computer-Assisted Intervention, 2015.PDF icon Article (2.24 MB)
B. Antic and Ommer, B., “Per-Sample Kernel Adaptation for Visual Recognition and Grouping”, in Proceedings of the IEEE International Conference on Computer Vision, 2015.PDF icon Technical Report (1.58 MB)
B. Antic and Ommer, B., “Spatio-temporal Video Parsing for Abnormality Detection”, arXiv, vol. abs/1502.06235, 2015.PDF icon Technical Report (4.61 MB)
B. Antic and Ommer, B., “Learning Latent Constituents for Recognition of Group Activities in Video”, in Proceedings of the European Conference on Computer Vision (ECCV) (Oral), 2014, p. 33--47.PDF icon Technical Report (4.54 MB)

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