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Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 472–480

Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86

Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp. Soc., Vancouver, Canada. 237–244

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad. Verlagsges., Berlin, Germany. 9 117–122

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc. Algebraic Frames for the Perception-Action Cycle. Springer, Kiel. 1888 164–174

Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388–400

Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision – ECCV 2004. Springer. 3024 74-86

Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. ijcv. 66 67-81

Cremers, D, Tischhäuser, F, Weickert, J and Schnörr, C (2002). Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional. Int. J. Computer Vision. 50 295–313

Chellappa, R and Machinery., Afor Comput (2010). Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010. ACM International Conference Proceeding Series. ACM

Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. Heidelberg University

Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568

Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060

Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498

Censor, Y, Gibali, A, Lenzen, F and Schnörr, C (2016). The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising. J. Comp. Math. 34 608-623

Cavallo, A (2002). Four dimensional particle tracking in biological dynamic processes. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/2471/

Carstens, H (1998). Ein Skalenraumverfahren Zur Orts/Wellenzahl-Raum-Analyse Winderzeugter Wasserwellen. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Carlsohn, M F, Menze, B H, Kelm, B Michael, Hamprecht, F A, Kercek, A, Leitner, R and Polder, G (2006). Color image processing. CRC Press. 7(17) 393-419

Jähne, B and Jähne, B (1991). Evaluation of a two-scale model using extensive radar backscatter and wave measurements in a large wind-wave flume. Proceedings IGARSS '91. 2 885--888

Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38

B

Bühl, M and Hamprecht, F A (1998). Theoretical Investigation of NMR Chemical Shifts and Reactivities of Oxovanadium (V) Compounds. Journal of Computational Chemistry. 19 113-122

Büchler, U, Brattoli, B and Ommer, B (2018). Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. Proceedings of the European Conference on Computer Vision (ECCV). (UB and BB contributed equally), Munich, Germany

Article (5.34 MB)

buechler_eccv18_poster.pdf (1.65 MB)

Brunswig, F (1992). Strukturanalyse Von Gletschereis Und Baumringen Mittels Digitaler Bildanalyse. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int.~J.~Computer Vision. 70 257-277

Technical Report (447.65 KB)

Bruhn, A, Jakob, T, Fischer, M, Kohlberger, T, Weickert, J, Brüning, U and Schnörr, C (2002). Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 290–297

Bruhn, A, Jakob, T, Fischer, M, Weickert, J, Brüning, U and Schnörr, C (2004). High performance cluster computing with 3-D nonlinear diffusion filters. Real-Time Imaging. 10 41–51

Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Real-Time Optic Flow Computation with Variational Methods. Proc. Computer Analysis of Images and Patterns (CAIP'03). Springer. 2756 222-229

Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2005). Variational optic flow computation in real-time. IEEE Trans. Image Proc. 14 608–615

Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Variational Optic Flow Computation In Real-Time. Dept. Math. and Comp. Science, Saarland University, Germany

Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int. J. Computer Vision. 70 257-277

Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2005). Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. Scale-Space 2005. Springer. 3459 279–290

Bruhn, A, Weickert, J and Schnörr, C (2002). Combining the Advantages of Local and Global Optic Flow Methods. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 454–462

Bruhn, A, Weickert, J and Schnörr, C (2005). Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. 61 211-231

Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg

Broecker, W S, Ledwell, J R, Takahashi, T, Weiss, R, Merlivat, L, Memery, L, Jähne, B and Münnich, K O (1986). Isotopic versus micrometeorologic ocean CO$_2$ fluxes: A serious conflict. J. Geophys. Res. 91 10517--10528

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