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[ Author(Desc)] Title Type Year
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K
Köthe, (2008). What Can We Learn from Discrete Images about the Continuous World. Discrete Geometry for Computer Imagery. Springer. 4992 4-19
Köthe, U, Herrmannsdörfer, F, Kats, I and Hamprecht, F A (2014). SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy. Histochemistry and Cell Biology. 141 613-627PDF icon Technical Report (2.29 MB)
Krasowski, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2015). Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors. 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015. 536-539PDF icon Technical Report (2.25 MB)
Kreshuk, A, Funke, J, Cardona, A and Hamprecht, F A (2015). Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain. MICCAI. Proceedings. Springer. LNCS 9349 661-668PDF icon Technical Report (2.14 MB)
Kreshuk, A, Stankiewicz, M, Lou, X, Kirchner, M, Hamprecht, F A and Mayer, M P (2010). Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon. International Journal of Mass Spectrometry. 302 125-131PDF icon Technical Report (3.22 MB)
Kreshuk, A, Straehle, C N, Sommer, C, Köthe, U, Knott, G W and Hamprecht, F A (2011). Automated Segmentation of Synapses in 3D EM Data. Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings. 220-223
Kreshuk, A, Straehle, C N, Sommer, C, Köthe, U, Cantoni, M, Knott, G W and Hamprecht, F A (2011). Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images. PLoS ONE. 6 (10)PDF icon Technical Report (290.48 KB)
Kreshuk, A, Walecki, R, Köthe, U, Gierthmühlen, M, Plachta, D, Genoud, C, Haastert-Talini, K and Hamprecht, F A (2015). Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves. Journal of Microscopy. 259 (2) 143-154
Kröger, T, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2014). Asymmetric Cuts: Joint Image Labeling and Partitioning. Pattern Recognition - 36th German Conference, {GCPR} 2014, Münster, Germany, September 2-5, 2014, Proceedings. http://dx.doi.org/10.1007/978-3-319-11752-2_16PDF icon Technical Report (3.46 MB)
Kröger, (2014). Learning-based Segmentation for Connectomics. University of Heidelberg
Kröger, (2010). Exploratory And Computational Analysis Of Huge Volume Images For Connectomics. University of Heidelberg
Kröger, T, Mikula, S, Denk, W, Köthe, U and Hamprecht, F A (2013). Learning to Segment Neurons with Non-local Quality Measures. MICCAI 2013. Proceedings, part II. Springer. 8150 419-427PDF icon Technical Report (2.87 MB)
Künsch, H R, Agrell, E and Hamprecht, F A (2005). Optimal lattices for sampling. IEEE Transactions on Information Theory. 51 634-647
L
Lenzen, F and Berger, J (2015). Solution-Driven Adaptive Total Variation Regularization. LNCS. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_17PDF icon Technical Report (857.29 KB)
Lerch, K (2006). Discontinuity Preserving Filtering Of Spectral Images. University of Heidelberg
Lichy, M P, Bachert, P, Hamprecht, F A, Weber, M - A, Debus, J, Schulz-Ertner, D, Kauczor, H - U and Schlemmer, H - P (2006). Einsatz der 1H-MR-spektroskopischen Bildgebung in der Strahlentherapie: Cholin als Marker für die Bestimmung der relativen Wahrscheinlichkeit eines Tumorprogresses nach Bestrahlung glialer Hirntumoren. Zeitung für Röntgenforschung. 178 627-633
Lindner, M (2011). A Machine Learning Approach To Improve Digital Embryo Analysis. University of Heidelberg
Lou, X (2011). Biomedical Data Analysis with Prior Knowledge: Modeling and Learning. University of Heidelberg
Lou, X, Fiaschi, L, Köthe, U and Hamprecht, F A (2012). Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MICCAI-MLMI. Proceedings. 176-183PDF icon Technical Report (4.15 MB)
M
Monroy, A, Bell, P and Ommer, B (2014). Morphological Analysis for Investigating Artistic Images. Image and Vision Computing. Elsevier. 32 414--423PDF icon Technical Report (2.86 MB)
Monroy, A, Bell, P and Ommer, B (2013). A Morphometric Approach to Reception Analysis of Premodern Art. Scientific Computing & Cultural HeritagePDF icon Technical Report (17.75 MB)
Monroy, A, Bell, P and Ommer, B (2012). Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions. Proceedings of the European Conference on Computer Vision, Workshop on VISART. Springer. 7583 571--580PDF icon Technical Report (7 MB)
Monroy, A, Carque, B and Ommer, B (2011). Reconstructing the Drawing Process of Reproductions from Medieval Images. Proceedings of the International Conference on Image Processing. IEEE. 2974--2977. https://hciweb.iwr.uni-heidelberg.de/compvis/research/manesse/PDF icon Technical Report (2.43 MB)
Monroy, A, Kröger, T, Arnold, M and Ommer, B (2011). Parametric Object Detection for Iconographic Analysis. Scientific Computing & Cultural Heritage. http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis
O
Ommer, B (2013). The Role of Shape in Visual Recognition. Shape Perception in Human Computer Vision: An Interdisciplinary Perspective. Springer. 373--385PDF icon Technical Report (8.18 MB)
Ommer, B and Buhmann, J M (2010). Learning the Compositional Nature of Visual Object Categories for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 32 501--516PDF icon Technical Report (2.78 MB)
Ommer, B, Mader, T and Buhmann, J M (2009). Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. International Journal of Computer Vision. Springer. 83 57--71PDF icon Technical Report (9.61 MB)
Ommer, B and Malik, J (2009). Multi-scale Object Detection by Clustering Lines. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 484--491PDF icon Technical Report (3.18 MB)
Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
Ommer, B and Buhmann, J M (2007). Compositional Object Recognition, Segmentation, and Tracking in Video. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 4679 318--333PDF icon Technical Report (2.78 MB)
Ommer, B and Buhmann, J M (2007). Learning the Compositional Nature of Visual Objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 1--8PDF icon Technical Report (2.78 MB)
Ommer, B, Sauter, M and M., B J (2006). Learning Top-Down Grouping of Compositional Hierarchies for Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision. IEEE. 194--194PDF icon Technical Report (358.98 KB)
Ommer, B and Buhmann, J M (2006). Learning Compositional Categorization Models. Proceedings of the European Conference on Computer Vision. Springer. 3953 316--329PDF icon Technical Report (1.35 MB)
Ommer, B and Buhmann, J M (2005). Object Categorization by Compositional Graphical Models. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 3757 235--250PDF icon Technical Report (2.07 MB)
Ommer, B and Buhmann, J M (2003). A Compositionality Architecture for Perceptual Feature Grouping. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 2683 275--290PDF icon Technical Report (2.89 MB)

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