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A
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)
B. Antic, Milbich, T., and Ommer, B., “Less is More: Video Trimming for Action Recognition”, in Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction, 2013, p. 515--521.PDF icon Technical Report (984.89 KB)
B. Antic and Ommer, B., “Robust Multiple-Instance Learning with Superbags”, in Proceedings of the Aian Conference on Computer Vision (ACCV) (Oral), 2012, p. 242--255.PDF icon Technical Report (319.58 KB)
B. Antic and Ommer, B., “Video Parsing for Abnormality Detection”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 2415--2422.PDF icon Technical Report (990.21 KB)
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)
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.
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.
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.
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.
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.
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 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 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, “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, “Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations”, vol. Dissertation. IWR, Univ. Heidelberg, 2013.
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.
B
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. Bailoni, “Deep Learning for Graph-Based Image Instance Segmentation”. Heidelberg University, 2021.
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.
L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.
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.
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.
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.

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