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W

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

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

D. Wierzimok, Jähne, B., and Dengler, J., “Bildfolgenanalyse dreidimensionaler turbulenter Strömungen”, in Proc. 9. DAGM-Symposium zur Mustererkennung 1987, 1987, vol. 149, p. 288.

R. Winter, “Fluorescent Tracers for air-sided Concentration Profile Measurements at the Air-Water Interface”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, 2011.

D. Withopf, “Reliable Real-Time Vehicle Detection and Tracking”. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg, 2007.

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

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

C. Wolf, “Surface Properties of Breaking Water Waves”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1994.

G. Wolf, “Aufbau einer Pilotanlage zur gaschromatographischen Tritiumanreicherung”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1981.

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

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

Technical Report (3.76 MB)

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

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

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

Technical Report (2.58 MB)

S. Wolf, “Machine Learning for Instance Segmentation”. Heidelberg University, 2020.

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

Technical Report (357.23 KB)

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

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

A. Wolny, 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., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.

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

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

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

M. Wulf, Stiehl, H. S., and Schnörr, C., “Modeling spatiotemporal receptive fields in the primate retina”, in Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999.

X

S. Xiao, “Tracking Dividing Cells Using Spatio-Temporal Embeddings”, Heidelberg University, 2019.

B. Xu, “Weakly supervised learning: Active schemes and partial annotations”, University of Heidelberg, 2013.

Y

J. Yarkony, Beier, T., Baldi, P., and Hamprecht, F. A., “Parallel Multicut Segmentation via Dual Decomposition”, in 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, 2014.

J. Yarkony, Zhang, C., and Fowlkes, C. C., “Hierarchical Planar Correlation Clustering for Cell Segmentation”, in EMMCVPR. Proceedings, 2014, vol. 8932, pp. 492-504.

Technical Report (548.12 KB)

P. Yarlagadda and Ommer, B., “Beyond the Sum of Parts: Voting with Groups of Dependent Entities”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, p. 1134--1147, 2015.

P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Towards a Computer-based Understanding of Medieval Images”, in Scientific Computing & Cultural Heritage, Springer, 2013, p. 89--97.

P. Yarlagadda and Ommer, B., “From Meaningful Contours to Discriminative Object Shape”, in Proceedings of the European Conference on Computer Vision, 2012, vol. 7572, p. 766--779.

Technical Report (4.58 MB)

P. Yarlagadda, Eigenstetter, A., and Ommer, B., “Learning Discriminative Chamfer Regularization”, in BMVC, 2012, p. 1--11.

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

Technical Report (11.06 MB)

P. Yarlagadda, Monroy, A., and Ommer, B., “Voting by Grouping Dependent Parts”, in Proceedings of the European Conference on Computer Vision, 2010, vol. 6315, p. 197--210.

Technical Report (2.99 MB)

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

Technical Report (2.76 MB)

P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Towards a Computer-based Understanding of Medieval Images”, in Scientific Computing & Cultural Heritage, 2009, p. 89--97.

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