Publications

J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.

F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”. 2016.

E. Meijering, Carpenter, A. E., Peng, H., Hamprecht, F. A., and Olivo-Marin, J., “Imagining the future of bioimage analysis”, Nature Biotechnology, vol. 34, no. 12, pp. 1250-1255, 2016.

Technical Report (924.57 KB)

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.

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.

J. Mund, Michel, F., Dieke-Meier, F., Fricke, H., Meyer, L., and Rother, C., “Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations”, in International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, 2016.

J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, 2016.

J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, Hannover, 2016.

Technical Report (2.34 MB)

A. Stefanoiu, Weinmann, A., Storath, M., Navab, N., and Baust, M., “Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm”, IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3384 - 3394, 2016.

Technical Report (3.55 MB)

M. Schiegg, Diego, F., and Hamprecht, F. A., “Learning Diverse Models: The Coulomb Structured Support Vector Machine”, ECCV. Proceedings, vol. LNCS 9907. Springer, pp. 585-599, 2016.

Technical Report (2.54 MB)

M. von Borstel, “Learning to Count from Weak Supervision”, University of Heidelberg, 2016.

M. Diebold, “Light-Field Imaging and Heterogeneous Light Fields”, vol. Dissertation. IWR, Univ. Heidelberg, 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.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

T. M. D. Strouse, “Marijuana's Public Health Pros and Cons | For Better | US News”, U.S. News and World Report, 2016.

S. Lenor, “Model-Based Estimation of Meteorological Visibility in the Context of Automotive Camera Systems”, vol. Dissertation. IWR, Univ. Heidelberg, 2016.

J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.

B. Jähne and Schwarzbauer, M., “Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?”, tm – Technisches Messen, vol. 83, pp. 16–24, 2016.

M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.

E. Bodnariuc, Petra, S., Poelma, C., and Schnörr, C., “Parametric Dictionary-Based Velocimetry for Echo PIV”, in Proc. CGPR, 2016.

P. Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370–1382, 2016.

P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.

E. Bodnariuc, Schiffner, M. F., Petra, S., and Schnörr, C., “Plane Wave Acoustic Superposition for Fast Ultrasound Imaging”, International Ultrasonics Symposium. 2016.

O. Hosseini Jafari and Yang, M. Ying, “Real-time RGB-D based template matching pedestrian detection”, in Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.

N. von Schmude, Lothe, P., and Jähne, B., “Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry”, in Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016.

C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.

Technical Report (4.46 MB)

D. Rathore, “Semantic Segmentation Using Deep Learning”, University of Heidelberg, 2016.

K. Schwarz, “Spatio-Temporal Measurements of Water-Wave Height and Slope using Laser-Induced Fluorescence and Splines”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2016.

A. Sellent, Rother, C., and Roth, S., “Stereo video deblurring”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9906 LNCS, pp. 558–575.

A. Sellent, Rother, C., and Roth, S., “Stereo Video Deblurring-Supplemental Material”, 2016.

A. Kiem, “Structured Learning on Calcium Imaging Data”, University of Heidelberg, 2016.

F. Diego and Hamprecht, F. A., “Structured Regression Gradient Boosting”, CVPR. Proceedings. pp. 1459-1467, 2016.

Technical Report (3.97 MB)

Pages