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2014
H. Schäfer, Lenzen, F., and Garbe, C. S., “Model based scattering correction in time-of-flight cameras”, Optics Express, vol. 22, pp. 29835-29846, 2014.
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
B. Krolla, Diebold, M., Goldlücke, B., and Stricker, D., “Spherical Light Fields”, in Proceedings of the British Machine Vision Conference, 2014.
D. Kondermann, Nair, R., Meister, S., Mischler, W., Güssefeld, B., Hofmann, S., Brenner, C., and Jähne, B., “Stereo ground truth with error bars”, in Asian Conference on Computer Vision, ACCV 2014, 2014.
B. Goldlücke, Aubry, M., Kolev, K., and Cremers, D., “A super-resolution framework for high-accuracy multiview reconstruction”, Int. J. Comp. Vision, vol. 106, p. 172--191, 2014.
M. Gutsche, “Surface Velocity Measurements at the Aeolotron by Means of Active Thermography”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
M. Gutsche, “Surface Velocity Measurements at the Aeolotron by Means of Active Thermography”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
J. - M. Gottfried, Nair, R., Meister, S., Garbe, C. S., and Kondermann, D., “Time of flight motion compensation revisited”, in International Conference on Image Processing, ICIP 2014, 2014.
J. - M. Gottfried, Nair, R., Meister, S., Garbe, C. S., and Kondermann, D., “Time of flight motion compensation revisited”, in International Conference on Image Processing, ICIP 2014, 2014.
L. Fiaschi, Diego, F., Grosser, K. - H., Schiegg, M., Köthe, U., Zlatic, M., and Hamprecht, F. A., “Tracking indistinguishable translucent objects over time using weakly supervised structured learning”, in CVPR. Proceedings, 2014, pp. 2736 - 2743.PDF icon Technical Report (1.47 MB)
S. Wanner and Goldlücke, B., “Variational light field analysis for disparity estimation and super-resolution”, IEEE Trans. Pattern Analysis Machine Intelligence, vol. 36, p. 606--619, 2014.
2015
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in Proc.~EMMCVPR, 2015, vol. 8932, p. 378--391.PDF icon Technical Report (951.37 KB)
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in EMMCVPR, 2015.
N. Gianniotis, Schnörr, C., Molkenthin, C., and Bora, S. S., “Approximate variational inference based on a finite sample of Gaussian latent variables”, Patt.~Anal.~Appl., 2015.PDF icon Technical Report (1.4 MB)
A. Kreshuk, Walecki, R., Köthe, U., Gierthmühlen, M., Plachta, D., Genoud, C., Haastert-Talini, K., and Hamprecht, F. A., “Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves”, Journal of Microscopy, vol. 259 (2), pp. 143-154, 2015.
A. Kreshuk, Walecki, R., Köthe, U., Gierthmühlen, M., Plachta, D., Genoud, C., Haastert-Talini, K., and Hamprecht, F. A., “Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves”, Journal of Microscopy, vol. 259 (2), pp. 143-154, 2015.
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.
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.
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.
D. Richmond, Kainmueller, D., Glocker, B., Rother, C., and Myers, G., “Uncertainty-driven forest predictors for vertebra localization and segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9349. pp. 653–660, 2015.
2016
B. Güssefeld, Honauer, K., and Kondermann, D., “Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets”, in Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}, 2016.
K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 2016.
H. Schilling, Diebold, M., Gutsche, M., Aziz-Ahmad, H., and Jähne, B., “A fractal calibration pattern for improved camera calibration”, Forum Bildverarbeitung. 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.
M. Diebold, Gatto, A., and Jähne, B., “Heterogeneous Light Fields”, in 2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016, 2016.
Y. Censor, Gibali, A., Lenzen, F., and Schnörr, C., “The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising”, J. Comp. Math., vol. 34, pp. 608-623, 2016.
P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., “Lost and found: Detecting small road hazards for self-driving vehicles”, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
2017
O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.

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