Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 103 results:

] Title Type Year

Filters: First Letter Of Last Name is L [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

L

C. Leue, Wenig, M., Jähne, B., and Platt, U., “Quantitative observation of biomass-burning plumes from GOME”, ESA Publications EOQ, vol. 58, p. 33--35, 1998.

C. Leue, Wenig, M., Jähne, B., and Platt, U., “GOME mißt atmosphärische Stickoxide. Globale Biomassenverbrennung und Industrieemissionen”, Physik in unserer Zeit, vol. 29, p. 179, 1998.

C. Leue, Wenig, M., Platt, U., Jähne, B., and Haußecker, H., “NOX Emissions Retrieved from Satellite Images”, Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press, p. 654--655, 2000.

C. Leue, Wenig, M., Wagner, T., Klimm, O., Platt, U., and Jähne, B., “Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences”, J. Geophys. Res., vol. 106, p. 5493--5505, 2001.

K. Lerch, “Discontinuity Preserving Filtering of Spectral Images”, University of Heidelberg, 2006.

F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.

Technical Report (857.29 KB)

F. Lenzen, Becker, F., and Lellmann, J., “Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, vol. 7893, pp. 61-73.

F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition”, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.

F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.

F. Lenzen, Kim, K. I., Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, in Time-of-Flight Imaging: Algorithms, Sensors and Applications, 2013, vol. 8200, pp. 24-25.

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.

F. Lenzen, Schäfer, H., and Garbe, C. S., “Denoising Time-Of-Flight Data with Adaptive Total Variation”, in Proceedings ISVC, 2011, pp. 337-346.

F. Lenzen, Becker, F., and Lellmann, J., “Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 54, no. 2, p. 371--398.

Technical Report (702.08 KB)

F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in LNCS, 2012, pp. 206-217.

Technical Report (649.03 KB)

F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A class of quasi-variational inequalities for adaptive image denoising and decomposition”, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.

Technical Report (748.66 KB)

F. Lenzen, Kim, K. In, Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 25-45, 2013.

Technical Report (961.62 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, p. 2139--2174, 2014.

Technical Report (802.13 KB)

F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, 2012, pp. 206-217.

F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.

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.

S. Lenor, Martini, J., Jähne, B., Stopper, U., Weber, S., and Ohr, F., “Tracking-based visibility estimation”, in Pattern Recognition, 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, 2014, vol. 8753, p. 365--376.

S. Lenor, “Model-Based Estimation of Meteorological Visibility in the Context of Automotive Camera Systems”, vol. Dissertation. IWR, Univ. Heidelberg, 2016.

V. Lempitsky, Roth, S., and Rother, C., “FusionFlow: Discrete-continuous optimization for optical flow estimation”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.

V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.

V. Lempitsky, Blake, A., and Rother, C., “Branch-and-mincut: Global optimization for image segmentation with high-level priors”, Journal of Mathematical Imaging and Vision, vol. 44, pp. 315–329, 2012.

V. Lempitsky, Blake, A., and Rother, C., “Image segmentation by branch-and-mincut”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.

V. Lempitsky, Blake, A., and Rother, C., “Image segmentation by branch-and-mincut”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5305 LNCS, pp. 15–29.

V. Lempitsky, Kohli, P., Rother, C., and Sharp, T., “Image segmentation with a bounding box prior”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 277–284.

V. Lempitsky, Rother, C., and Blake, A., “LogCut - Efficient graph cut optimization for markov random fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.

J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, 2009, pp. 646-653.

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for Variational Relaxations of Optimal Partition Problems”, 2010.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, pp. 132-146.

Pages