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[ Author(Desc)] Title Type Year
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M. Brocke, “Statistical Image Sequence Processing for Temporal Change Detection”, in Proceedings of the 24th DAGM Symposium on Pattern Recognition, 2002, vol. LNCS 2449, p. 215--223.
M. Brocke, “Statistische Ereignisdetektion in Bildfolgen”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2002.
M. Brocke, “Bildverarbeitung für Mikrolaserschweißen unter Verwendung der Houghtransformation”, Universität Heidelberg, 1998.
W. S. Broecker, Ledwell, J. R., Takahashi, T., Weiss, R., Merlivat, L., Memery, L., Jähne, B., and Münnich, K. O., “Isotopic versus micrometeorologic ocean CO$_2$ fluxes: A serious conflict”, J. Geophys. Res., vol. 91, p. 10517--10528, 1986.
M. Brosowsky, “Cluster Resolving for Animal Tracking: Multi Hypotheses Tracking with Part Based Model for Object Hypotheses Generation and Pose Estimation”, University of Heidelberg, 2017.
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.PDF icon Technical Report (447.65 KB)
A. Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C., “Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290–297.
A. Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., “High performance cluster computing with 3-D nonlinear diffusion filters”, Real-Time Imaging, vol. 10, pp. 41–51, 2004.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Real-Time Optic Flow Computation with Variational Methods”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 222-229.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational optic flow computation in real-time”, IEEE Trans. Image Proc., vol. 14, pp. 608–615, 2005.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational Optic Flow Computation in Real-Time”, Dept. Math. and Comp. Science, Saarland University, Germany, 89, 2003.
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time”, in Scale-Space 2005, 2005, vol. 3459, pp. 279–290.
A. Bruhn, Weickert, J., and Schnörr, C., “Combining the Advantages of Local and Global Optic Flow Methods”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454–462.
A. Bruhn, Weickert, J., and Schnörr, C., “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods”, vol. 61, pp. 211-231, 2005.
F. Brunswig, “Strukturanalyse von Gletschereis und Baumringen mittels Digitaler Bildanalyse”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1992.
U. Büchler, Brattoli, B., and Ommer, B., “Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning”, in Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018.PDF icon Article (5.34 MB)PDF icon buechler_eccv18_poster.pdf (1.65 MB)
M. Bühl and Hamprecht, F. A., “Theoretical Investigation of NMR Chemical Shifts and Reactivities of Oxovanadium (V) Compounds”, Journal of Computational Chemistry, vol. 19, pp. 113-122, 1998.
C
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
B. Jähne and Jähne, B., “Evaluation of a two-scale model using extensive radar backscatter and wave measurements in a large wind-wave flume”, in Proceedings IGARSS '91, 1991, vol. 2, p. 885--888.
M. F. Carlsohn, Menze, B. H., Kelm, B. Michael, Hamprecht, F. A., Kercek, A., Leitner, R., and Polder, G., “Color image processing”, vol. 7(17), R. Lukac and Plataniotis, K. N., Eds. CRC Press, 2006, pp. 393-419.
H. Carstens, “Ein Skalenraumverfahren zur Orts/Wellenzahl-Raum-Analyse winderzeugter Wasserwellen”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.
A. Cavallo, “Four dimensional particle tracking in biological dynamic processes”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2002.
Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, J. Appl. Numer. Optimization (in press; arXiv:1911.05498), vol. 2, pp. 15-62, 2020.
Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.
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.
L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.
L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.
R. Chellappa and Machinery., Afor Comput, “Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010”, ACM International Conference Proceeding Series. ACM, 2010.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.PDF icon Technical Report (636.58 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.PDF icon Technical Report (324.55 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.PDF icon Technical Report (451.82 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

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