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2015
Krasowski, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2015). Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors. 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015. 536-539PDF icon Technical Report (2.25 MB)
Krull, A, Brachmann, E, Michel, F, Yang, M Ying, Gumhold, S and Rother, C (2015). Learning analysis-by-synthesis for 6d pose estimation in RGB-D images. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 954–962
Diebold, M, Blum, O, Gutsche, M, Wanner, S, Garbe, C S, Baker, H and Jähne, B (2015). Light-field camera design for high-accuracy depth estimation. Videometrics, Range Imaging, and Applications XIII
Diebold, M, Blum, O, Gutsche, M, Wanner, S, Garbe, C S, Baker, H and Jähne, B (2015). Light-field camera design for high-accuracy depth estimation. Videometrics, Range Imaging, and Applications XIII
Diebold, M, Blum, O, Gutsche, M, Wanner, S, Garbe, C, Baker, H and Jähne, B (2015). Light-field camera design for high-accuracy depth estimation. Videometrics, Range Imaging, and Applications XIII. SPIE
Diebold, M, Blum, O, Gutsche, M, Wanner, S, Garbe, C, Baker, H and Jähne, B (2015). Light-field camera design for high-accuracy depth estimation. Videometrics, Range Imaging, and Applications XIII. SPIE
Mesarchaki, E, Kräuter, C, Krall, K Ellen, Bopp, M, Helleis, F, Williams, J and Jähne, B (2015). Measuring air–sea gas-exchange velocities in a large-scale annular wind–wave tank. Ocean Sci. 11 121--138
Michel, F, Krull, A, Brachmann, E, Yang, M Ying, Gumhold, S and Rother, C (2015). Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. 181.1–181.11
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015)
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810
Lenzen, F and Berger, J (2015). Solution-Driven Adaptive Total Variation Regularization. LNCS. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_17PDF icon Technical Report (857.29 KB)
Lenzen, F and Berger, J (2015). Solution-Driven Adaptive Total Variation Regularization. LNCS
Antic, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. SpringerPDF icon Article (2.24 MB)
Kondermann, D, Nair, R, Meister, S, Mischler, W, Güssefeld, B, Honauer, K, Hofmann, S, Brenner, C and Jähne, B (2015). Stereo Ground Truth with Error Bars. Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V. Springer International Publishing, Cham. 595–610. http://dx.doi.org/10.1007/978-3-319-16814-2_39
Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38
Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38
Bell, P and Ommer, B (2015). Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte. 68 414--420
2016
Bautista, M, Sanakoyeu, A, Sutter, E and Ommer, B (2016). CliqueCNN: Deep Unsupervised Exemplar Learning. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Barcelona. https://arxiv.org/abs/1608.08792PDF icon Article (5.79 MB)
Baust, M, Weinmann, A, Wieczorek, M, Lasser, T, Storath, M and Navab, N (2016). Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach. IEEE Transactions on Medical Imaging. 35 1972–1989PDF icon Technical Report (8.65 MB)
Balles, L (2016). Deep Learning For Diabetic Retinopathy Diagnostics. University of Heidelberg
Kleesiek, J, Urban, G, Hubert, A, Schwarz, D, Maier-Hein, K, Bendszus, M and Biller, A (2016). Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.. NeuroImage. 129 460-469PDF icon Technical Report (1.14 MB)
Kleesiek, J, Urban, G, Hubert, A, Schwarz, D, Maier-Hein, K, Bendszus, M and Biller, A (2016). Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.. NeuroImage. 129 460-469PDF icon Technical Report (1.14 MB)
Bell, P and Ommer, B (2016). Digital Connoisseur? How Computer Vision Supports Art History. Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.). Artemide, Rome
Beier, T, Andres, B, Köthe, U and Hamprecht, F A (2016). An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV. Proceedings. Springer. LNCS 9906 715-730PDF icon Technical Report (4.89 MB)
Kondermann, D, Nair, R, Honauer, K, Krispin, K, Andrulis, J, Brock, A, Güssefeld, B, Rahimimoghaddam, M, Hofmann, S, Brenner, C and Jähne, B (2016). The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Kondermann, D, Nair, R, Honauer, K, Krispin, K, Andrulis, J, Brock, A, Güssefeld, B, Rahimimoghaddam, M, Hofmann, S, Brenner, C and Jähne, B (2016). The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Biller, A, Badde, S, Nagel, A, Neumann, J O, Wick, W, Hertenstein, A, Bendszus, M, Sahm, F, Benkhedah, N and Kleesiek, J (2016). Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression. American Journal of Neuroradiology. 37 66-73
Biller, A, Badde, S, Nagel, A, Neumann, J O, Wick, W, Hertenstein, A, Bendszus, M, Sahm, F, Benkhedah, N and Kleesiek, J (2016). Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression. American Journal of Neuroradiology. 37 66-73
Biller, A, Badde, S, Nagel, A, Neumann, J O, Wick, W, Hertenstein, A, Bendszus, M, Sahm, F, Benkhedah, N and Kleesiek, J (2016). Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression. American Journal of Neuroradiology. 37 66-73

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