In Press

Weiler, M, Hamprecht, F A and Storath, M (In Press). Learning Steerable Filters for Rotation Equivariant CNNs . CVPR (accepted)
Erb, W, Weinmann, A, Ahlborg, M, Brandt, C, Bringout, G, Buzug, T M, Frikel, J, Kaethner, C, Knopp, T, März, T, Möddel, M, Storath, M and Weber, A (In Press). Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems
Bredies, K, Holler, M, Storath, M and Weinmann, A (In Press). Total Generalized Variation for Manifold-valued Data. SIAM Journal on Imaging Sciences


Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., Project Page
Lang, S and Ommer, B (2018). Attesting Similarity: Supporting the Orgainzation and Study of Art Image Collections with Computer Vision. Digital Scholarship in the Humanities,
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. , Arxiv Paper
Weilbach, C (2018). Dictionary Learning With Bayesian Gans For Few-Shot Classification. Heidelberg University
Wahl, A S, Erlebach, E, Brattoli, B, Büchler, U, Kaiser, J, Ineichen, V B, Mosberger, A C, Schneeberger, S, Imobersteg, S, Wieckhorst, M, Stirn, M, Schroeter, A, Ommer, B and Schwab, M E (2018). Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats. Sage Journals. Journal of Cerebral Blood Flow & Metabolism. icon 0271678x18777661.pdf (770.87 KB)
Draxler, F, Veschgini, K, Salmhofer, M and Hamprecht, F A (2018). Essentially No Barriers in Neural Network Energy Landscape. ICML. Proceedings, in pressPDF icon Technical Report (685.93 KB)
Storath, M and Weinmann, A (2018). Fast median filtering for phase or orientation data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 639–652PDF icon Technical Report (7.32 MB), Circle Median Filter
Schimmel, F (2018). Learnability Of Approximated Graph Cut Segmentation. Heidelberg University
Ghori, O, Mackowiak, R, Bautista, M, Beuter, N, Drumond, L, Diego, F and Ommer, B (2018). Learning to Forecast Pedestrian Intention from Pose Dynamics. Intelligent Vehicles, IEEE, 2018
Kiechle, M, Storath, M, Weinmann, A and Kleinsteuber, M (2018). Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing. 27 1994-2007
Beier, T (2018). Multicut Algorithms for Neurite Segmentation. Heidelberg University
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University


Vianello, A, Manfredi, G, Diebold, M and Jähne, B (2017). 3D reconstruction by a combined structure tensor and Hough transform light field approach. tm - Technisches Messen
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Ulman, V, Maška, M, Magnusson, K E G, Ronneberger, O, Haubold, C, Harder, N, Matula, P, Matula, P, Svoboda, D, Radojevic, M, Smal, I, Rohr, K, Jaldén, J, Blau, H M, Dzyubachyk, O, Lelieveldt, B, Xiao, P, Li, Y, Cho, S - Y, Dufour, A, Olivo-Marin, J C, Reyes-Aldasoro, C C, Solis-Lemus, J A, Bensch, R, Brox, T, Stegmaier, J, Mikut, R, Wolf, S, Hamprecht, F A, Esteves, T, Quelhas, P, Demirel, Ö, Malström, L, Jug, F, Tomančák, P, Meijering, E, Muñoz-Barrutia, A, Kozubek, M and Ortiz-de-Solorzano, C (2017). An Objective Comparison of Cell Tracking Algorithms. Nature Methods. 14 1141-1152PDF icon Technical Report (4.24 MB)
Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Ufer, N and Ommer, B (2017). Deep Semantic Feature Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon article (8.88 MB)
Bautista, M, Sanakoyeu, A and Ommer, B (2017). Deep Unsupervised Similarity Learning using Partially Ordered Sets. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)
Haubold, C, Uhlmann, V, Unser, M and Hamprecht, F A (2017). Diverse M-best Solutions by Dynamic Programming. GCPR. Proceedings. Springer. LNCS 10496 255-267
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
Storath, M, Brandt, C, Hofmann, M, Knopp, T, Salamon, J, Weber, A and Weinmann, A (2017). Edge preserving and noise reducing reconstruction for magnetic particle imaging. IEEE Transactions on Medical Imaging. 36 74 - 85PDF icon Technical Report (1.43 MB)
Storath, M, Rickert, D, Unser, M and Weinmann, A (2017). Fast segmentation from blurred data in 3D fluorescence microscopy. IEEE Transactions on Image Processing. 26
Hennies, J (2017). Improvement And Validation Of Neural Em Volume Image Segmentation By High-Level Information. University of Heidelberg
Haller, A (2017). Interactive Watershed Based Segmentation For Biological Images. University of Heidelberg
Storath, M, Weinmann, A and Unser, M (2017). Jump-penalized least absolute values estimation of scalar or circle-valued signals. Information and Inference. 6 225–245PDF icon Technical Report (3.4 MB)
Schott, L (2017). Learned Watershed Algorithm: End-To-End Learning Of Seeded Segmentation. Heidelberg University
Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038PDF icon Technical Report (3.76 MB)
Weiler, M (2017). Learning Steerable Filters For Rotation Equivariant Convolutional Neural Networks. Heidelberg University
Bautista, M, Fuchs, P and Ommer, B (2017). Learning Where to Drive by Watching Others. Proceedings of the German Conference Pattern Recognition. Springer-Verlag, Basel. 1, Learning Where to Drive by Watching Others
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. MICCAI. Proceedings. 177-184PDF icon Technical Report (4.79 MB)
Brattoli, B, Büchler, U, Wahl, A S, Schwab, M E and Ommer, B (2017). LSTM Self-Supervision for Detailed Behavior Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB contributed equally)PDF icon Article (8.75 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
Balluff, B, Hanselmann, M and Heeren, R M A (2017). Mass spectrometry imaging for the investigation of intratumor heterogeneity. Advances in Cancer Research. Elsevier. 134 201-230
Beier, T, Pape, C, Rahaman, N, Prange, T, Berg, S, Bock, D, Cardona, A, Knott, G W, Plaza, S M, Scheffer, L K, Köthe, U, Kreshuk, A and Hamprecht, F A (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods. 14 101-102.
Krasowki, N, Beier, T, Knott, G, Köthe, U, Hamprecht, F A and Kreshuk, A (2017). Neuron Segmentation with High-Level Biological Priors. IEEE Transactions on Medical Imaging. 37