Export 1490 results:
Author Title Type [ Year(Asc)]
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
Bredies, K, Holler, M, Storath, M and Weinmann, A (2018). Total Generalized Variation for Manifold-valued Data. SIAM Journal on Imaging Sciences. 11 1785 - 1848
Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (HBUGEN)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR. Proceedings. 698-713PDF icon Technical Report (5.23 MB)
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) (short Oral).
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)
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
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
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
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
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)
Wahl, A S, Büchler, U, Brändli, A, Brattoli, B, Musall, S, Kasper, H, Ineichen, B V, Helmchen, F, Ommer, B and Schwab, M E (2017). Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation. Nature Communications. (ASW & UB contributed equally; BO and MES contributed equally).
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Haubold, C (2017). Scalable Inference for Multi-Target Tracking on Proliferating Cells. University of Heidelberg