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Lalonde, J François, Hoiem, D, Efros, A A, Rother, C, Winn, J and Criminisi, A (2007). Photo clip art. Proceedings of the ACM SIGGRAPH Conference on Computer Graphics. http://graphics.cs.cmu.edu/projects/photoclipart/
Lang, S and Ommer, B (2020). Das Objekt jenseits der Digitalisierung. Das digitale Objekt. 7. http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdfPDF icon lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)
Lang, S and Ommer, B (2018). Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision. Digital Scholarship in the Humanities, Oxford University Press. 33 845-856
Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64PDF icon arts-07-00064.pdf (4.6 MB)
Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV - VISART). Springer
Lange, P A, Jähne, B, Tschiersch, J and Ilmberger, J (1982). Comparison between an amplitude-measuring wire and a slope-measuring laser water wave gauge. Rev. Sci. Instrum. 53 651--655
Lauer, H (1998). Untersuchung der Neigungsstatistik von Wasseroberflächenwellen mittels eines schnellen, bildaufnehmenden Verfahrens. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Lauer, H (1994). Messung Der Neigungsverteilung Von Wasseroberflächenwellen Mittels Digitaler Bilverarbeitung. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09)PDF icon Technical Report (1.12 MB)
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV'09). Kyoto, Japan
Lauer, F, Bloch, G and Vidal, R (2009). A Continuous Optimization Framework for Hybrid System Identification. submitted to Automatica
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press. 678-685
Lefloch, D, Nair, R, Lenzen, F, Schäfer, H, Streeter, L and Cree, M J (2013). Technical Foundation and Calibration Methods for Time-of-Flight Cameras. Time-of-Flight Imaging: Algorithms, Sensors and Applications. Springer. 8200
Lefloch, D, Nair, R, Lenzen, F, Schäfer, H, Streeter, L, Cree, M J, Koch, R and Kolb, A (2013). Technical Foundation and Calibration Methods for Time-of-Flight Cameras. Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications. Springer. 8200 3-24
Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249–257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036PDF icon PDF (8.94 MB)
Lell, M (1996). Ortsaufgelöste Bestimmung Von Blattwachstum Durch Strukturanalyse Von Bildsequenzen Aus Dem Nahen Infrarot. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Lellmann, J, Breitenreicher, D and Schnörr, C (2010). Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6312 494–505
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 6819 132–146
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds For A Variational Relaxation Of The Image Partitioning Problem. IPA group, Heidelberg University. http://arxiv.org/abs/1112.0974
Lellmann, J, Lenzen, F and Schnörr, C (2012). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. Springer. 47 239-257
Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. CoRR. abs/1102.5448. http://arxiv.org/abs/1102.5448
Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43–54
Lellmann, J and Schnörr, C (2010). Continuous Multiclass Labeling Approaches And Algorithms. Univ. of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/10460/
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Lellmann, J, Lenzen, F and Schnörr, C (2013). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Journal of Mathematical Imaging and Vision. 47 (3) 239-257
Lellmann, J, Lenzen, F and Schnörr, C (2010). Optimality Bounds for Variational Relaxations of Optimal Partition Problems
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 132-146
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)

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