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2014
J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.PDF icon Technical Report (405.91 KB)
J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.PDF icon Technical Report (405.91 KB)
F. Blumenthal, “Information-Geometric Optimization for Image Segmentation”, University of Heidelberg, 2014.
M. Bopp, “Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
M. Bopp, “Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.PDF icon Technical Report (557.49 KB)
J. Hendrik Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in International Workshop on Graphical Models in Computer Vision, 2014.
E. Mesarchaki, Kräuter, C., Krall, K. Ellen, Bopp, M., Helleis, F., Williams, J., and Jähne, B., “Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank”, Ocean Sci. Discuss., vol. 11, p. 1643--1689, 2014.
A. Monroy, Bell, P., and Ommer, B., “Morphological Analysis for Investigating Artistic Images”, Image and Vision Computing, vol. 32, p. 414--423, 2014.PDF icon Technical Report (2.86 MB)
G. Urban, Bendszus, M., Hamprecht, F. A., and Kleesiek, J., “Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution, 2014, pp. 31-35.
P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.
M. Takami, Bell, P., and Ommer, B., “Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM”, in Winter Conference on Applications of Computer Vision, 2014, p. 377--384.
F. Becker, Petra, S., and Schnörr, C., “Optical Flow”, Handbook of Mathematical Methods in Imaging. Springer, 2014.
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, 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.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.PDF icon Technical Report (802.13 KB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J. Imag. Sci., vol. 7, pp. 2139–2174, 2014.
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving QVIs for Image Restoration with Adaptive Constraint Sets”, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.
D. Kondermann, Nair, R., Meister, S., Mischler, W., Güssefeld, B., Hofmann, S., Brenner, C., and Jähne, B., “Stereo ground truth with error bars”, in Asian Conference on Computer Vision, ACCV 2014, 2014.
2015
A. Biesdorf, Wörz, S., von Tengg-Kobligk, H., Rohr, K., and Schnörr, C., “3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization”, in Proc.~ISBI, 2015.PDF icon Technical Report (611.33 KB)
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in Proc.~EMMCVPR, 2015, vol. 8932, p. 378--391.PDF icon Technical Report (951.37 KB)
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in EMMCVPR, 2015.
N. Krah, Testa, M., Brons, S., Jäkel, O., Parodi, K., Voss, B., and Rinaldi, I., “An advanced image processing method to improve the spatial resolution of ion radiographies”, Physics in Medicine and Biology, vol. 60, p. 8525, 2015.
N. Gianniotis, Schnörr, C., Molkenthin, C., and Bora, S. S., “Approximate variational inference based on a finite sample of Gaussian latent variables”, Patt.~Anal.~Appl., 2015.PDF icon Technical Report (1.4 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.PDF icon Technical Report (1.5 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.PDF icon Technical Report (5.12 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
A. Neufeld, Berger, J., Becker, F., Lenzen, F., and Schnörr, C., “Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework”, in 37th German Conference on Pattern Recognition, 2015.
A. Neufeld, Berger, J., Becker, F., Lenzen, F., and Schnörr, C., “Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework”, in 37th German Conference on Pattern Recognition, 2015.
T. Beier, Hamprecht, F. A., and Kappes, J. H., “Fusion Moves for Correlation Clustering”, in CVPR. Proceedings, 2015, pp. 3507-3516.PDF icon Technical Report (1.19 MB)

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