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Wierzimok, D and Jähne, B (1990). Automatic particle tracking beneath a wind-stressed wavy water surface with image processing. Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989. 943--956

Wierzimok, D and Jähne, B (1989). Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing. 5th International Symposium on Flow Visualization

Wierzimok, D, Jähne, B and Dengler, J (1987). Bildfolgenanalyse dreidimensionaler turbulenter Strömungen. Proc. 9. DAGM-Symposium zur Mustererkennung 1987. Springer. 149 288

Winter, R (2011). Fluorescent Tracers for air-sided Concentration Profile Measurements at the Air-Water Interface. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg. Dissertation. http://www.ub.uni-heidelberg.de/archiv/12105

Withopf, D (2007). Reliable Real-Time Vehicle Detection and Tracking. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg. http://d-nb.info/98745398X

Withopf, D and Jähne, B (2007). Improved training algorithm for tree-like classifiers and its application to vehicle detection. Proc. IEEE Intelligent Transportation Systems Conference (ITSC). 642--647

Withopf, D and Jähne, B (2006). Learning algorithm for real-time vehicle tracking. Proc. IEEE Intelligent Transportation Systems Conference ITSC '06. 516--521

Wolf, C (1994). Surface Properties Of Breaking Water Waves. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Wolf, G (1981). Aufbau Einer Pilotanlage Zur Gaschromatographischen Tritiumanreicherung. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Wolf, S (2016). Cell Tracking With Graphical Model Using Higher Order Features On Track Segments. University of Heidelberg

Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038

Technical Report (3.76 MB)

Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings. Springer. 571-587

Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654

Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2020). The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43 3724-3738

Technical Report (2.58 MB)

Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University

Wolf, S, Hamprecht, F A and Funke, J (2020). Inpainting Networks Learn to Separate Cells in Microscopy Images. BMCV

Technical Report (357.23 KB)

Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020). The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV. Proceedings. 208-224

Wolf, S, Hamprecht, F A and Funke, J (2020). Instance Separation Emerges from Inpainting. arXiv preprint arXiv:2003.00891

Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, Vilches-Barro, A, Louveaux, M, Wenzel, C, Strauss, S, Wilson-Sanchez, D, Lymbouridou, R, Steigleder, S S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Tsiantis, M, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020). Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution. eLife. 9

Woodford, O J (2009). A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536. Optimization

Wulf, M, Stiehl, H S and Schnörr, C (2000). On the computational rôle of the primate retina. Proc. 2nd ICSC Symposium on Neural Computation (NC 2000). Berlin, Germany

Wulf, M, Stiehl, H S and Schnörr, C (1999). A model of spatiotemporal receptive fields in the primate retina. Proc. 1st Göttingen Conf. German Neurosci. Soc.. II

Wulf, M, Stiehl, H S and Schnörr, C (1999). Modeling spatiotemporal receptive fields in the primate retina. Proc. Cognitive Neurosci. Conf. Hanse–Wissenschaftskolleg, Bremen, Germany

X

Xiao, S (2019). Tracking Dividing Cells Using Spatio-Temporal Embeddings. Heidelberg University

Xu, B (2013). Weakly Supervised Learning: Active Schemes And Partial Annotations. University of Heidelberg

Y

Yarkony, J, Beier, T, Baldi, P and Hamprecht, F A (2014). Parallel Multicut Segmentation via Dual Decomposition. New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers. http://dx.doi.org/10.1007/978-3-319-17876-9_4

Yarkony, J, Zhang, C and Fowlkes, C C (2014). Hierarchical Planar Correlation Clustering for Cell Segmentation. EMMCVPR. Proceedings. Springer. 8932 492-504

Technical Report (548.12 KB)

Yarlagadda, P and Ommer, B (2015). Beyond the Sum of Parts: Voting with Groups of Dependent Entities. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 37 1134--1147. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6926849

Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2013). Towards a Computer-based Understanding of Medieval Images. Scientific Computing & Cultural Heritage. Springer. 89--97. http://link.springer.com/chapter/10.1007/978-3-642-28021-4_10

Yarlagadda, P and Ommer, B (2012). From Meaningful Contours to Discriminative Object Shape. Proceedings of the European Conference on Computer Vision. Springer. 7572 766--779

Technical Report (4.58 MB)

Yarlagadda, P, Eigenstetter, A and Ommer, B (2012). Learning Discriminative Chamfer Regularization. BMVC. Springer. 1--11. http://www.bmva.org/bmvc/2012/BMVC/paper020/paper020.pdf

Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2011). Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections. Conference on Computer Vision and Image Analysis of Art II. 7869 61--69

Technical Report (11.06 MB)

Yarlagadda, P, Monroy, A and Ommer, B (2010). Voting by Grouping Dependent Parts. Proceedings of the European Conference on Computer Vision. Springer. 6315 197--210

Technical Report (2.99 MB)

Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2010). Recognition and Analysis of Objects in Medieval Images. Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage. Springer. 296--305

Technical Report (2.76 MB)

Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2009). Towards a Computer-based Understanding of Medieval Images. Scientific Computing & Cultural Heritage. Springer. 89--97. http://link.springer.com/chapter/10.1007%2F978-3-642-28021-4_10#page-1

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