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Uttenweiler, D, Veigel, C, Steubing, R, Götz, C, Mann, S, Haußecker, H, Jähne, B and Fink, R H A (2000). Motion determination in actin filament fluorescence images with a spatio-temporal orientation analysis method.. Biophys J. Institut für Physiologie und Pathophysiologie, Ruprecht-Karls-Universität Heidelberg, 69120 Heidelberg, Germany. 78 2709--2715
Uttenweiler, D, Weber, C, Jähne, B, Fink, R H A and Schaar, H (2003). Spatiotemporal anisotropic diffusion filtering to improve signal-to-noise ratios and object restoration in fluorescence microscopic image sequences.. J Biomed Opt. Ruprecht-Karls-Universität Heidelberg, Institut für Physiologie und Pathophysiologie Medical Biophysics, Im Neuenheimer Feld 326, 69120 Heidelberg, Germany. 8 40--47.
Urban, G (2014). Neural Networks: Optimization And Applications. University of Heidelberg
Urban, G, Bendszus, M, Hamprecht, F A and Kleesiek, J (2014). Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution. 31-35
Ulman, V, Maška, M, Magnusson, K E G, Ronneberger, O, Haubold, C, Harder, N, Matula, P, Matula, P, Svoboda, D, Radojevic, M, Smal, I, Rohr, K, Jaldén, J, Blau, H M, Dzyubachyk, O, Lelieveldt, B, Xiao, P, Li, Y, Cho, S - Y, Dufour, A, Olivo-Marin, J C, Reyes-Aldasoro, C C, Solis-Lemus, J A, Bensch, R, Brox, T, Stegmaier, J, Mikut, R, Wolf, S, Hamprecht, F A, Esteves, T, Quelhas, P, Demirel, Ö, Malström, L, Jug, F, Tomančák, P, Meijering, E, Muñoz-Barrutia, A, Kozubek, M and Ortiz-de-Solorzano, C (2017). An Objective Comparison of Cell Tracking Algorithms. Nature Methods. 14 1141-1152PDF icon Technical Report (4.24 MB)
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
Ufer, N and Ommer, B (2017). Deep Semantic Feature Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon article (8.88 MB)
Ufer, N, Lui, K To, Schwarz, K, Warkentin, P and Ommer, B (2019). Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation. German Conference on Pattern Recognition (GCPR)PDF icon article (6.1 MB)
Ufer, N, Lang, S and Ommer, B (2020). Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting. IEEE European Conference on Computer Vision (ECCV), VISART Workshop PDF icon Paper (1.03 MB)