HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 501 results:
Author Title Type [ Year(Desc)]
Filters: First Letter Of Last Name is B  [Clear All Filters]
2015
N. Krasowski, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors”, in 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015, 2015, pp. 536-539.PDF icon Technical Report (2.25 MB)
A. Krull, Brachmann, E., Michel, F., Yang, M. Ying, Gumhold, S., and Rother, C., “Learning analysis-by-synthesis for 6d pose estimation in RGB-D images”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 954–962.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C. S., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, Videometrics, Range Imaging, and Applications XIII. 2015.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C. S., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, Videometrics, Range Imaging, and Applications XIII. 2015.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, in Videometrics, Range Imaging, and Applications XIII, 2015.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, in Videometrics, Range Imaging, and Applications XIII, 2015.
E. Mesarchaki, Kräuter, C., Krall, K. Ellen, Bopp, M., Helleis, F., Williams, J., and Jähne, B., “Measuring air–sea gas-exchange velocities in a large-scale annular wind–wave tank”, Ocean Sci., vol. 11, p. 121--138, 2015.
F. Michel, Krull, A., Brachmann, E., Yang, M. Ying, Gumhold, S., and Rother, C., “Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression”, 2015, pp. 181.1–181.11.
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.PDF icon Technical Report (364.01 KB)
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.PDF icon Technical Report (364.01 KB)
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.PDF icon Technical Report (4.42 MB)
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.PDF icon Technical Report (4.42 MB)
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.
F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.PDF icon Technical Report (857.29 KB)
F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.
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)
D. Kondermann, Nair, R., Meister, S., Mischler, W., Güssefeld, B., Honauer, K., Hofmann, S., Brenner, C., and Jähne, B., “Stereo Ground Truth with Error Bars”, in Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V, Cham: Springer International Publishing, 2015, pp. 595–610.
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
P. Bell and Ommer, B., “Training Argus”, Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege, vol. 68, p. 414--420, 2015.
2016
M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.PDF icon Article (5.79 MB)
M. Baust, Weinmann, A., Wieczorek, M., Lasser, T., Storath, M., and Navab, N., “Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach”, IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1972–1989, 2016.PDF icon Technical Report (8.65 MB)
L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.
J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.PDF icon Technical Report (1.14 MB)
J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.PDF icon Technical Report (1.14 MB)
P. Bell and Ommer, B., “Digital Connoisseur? How Computer Vision Supports Art History”, in Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
A. Biller, Badde, S., Nagel, A., Neumann, J. O., Wick, W., Hertenstein, A., Bendszus, M., Sahm, F., Benkhedah, N., and Kleesiek, J., “Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression”, American Journal of Neuroradiology, vol. 37 , pp. 66-73, 2016.
A. Biller, Badde, S., Nagel, A., Neumann, J. O., Wick, W., Hertenstein, A., Bendszus, M., Sahm, F., Benkhedah, N., and Kleesiek, J., “Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression”, American Journal of Neuroradiology, vol. 37 , pp. 66-73, 2016.
A. Biller, Badde, S., Nagel, A., Neumann, J. O., Wick, W., Hertenstein, A., Bendszus, M., Sahm, F., Benkhedah, N., and Kleesiek, J., “Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression”, American Journal of Neuroradiology, vol. 37 , pp. 66-73, 2016.

Pages