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[ Author(Asc)] Title Type Year
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F
Frank, M and Hamprecht, F A (2011). Image-Based Supervision of a Periodically Working Machine. Pattern Analysis and Applications. 1-10PDF icon Technical Report (466.61 KB)
Frank, M, Plaue, M and Hamprecht, F A (2009). Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures. Optical Engineering. 48, 077003PDF icon Technical Report (2.5 MB)
Fiaschi, L, Diego, F, Grosser, K - H, Schiegg, M, Köthe, U, Zlatic, M and Hamprecht, F A (2014). Tracking indistinguishable translucent objects over time using weakly supervised structured learning. CVPR. Proceedings. 2736 - 2743PDF icon Technical Report (1.47 MB)
Fiaschi, L, Grosser, K - H, Afonso, B, Zlatic, M and Hamprecht, F A (2013). Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects. ISBI 2013.Proceedings. 656-659PDF icon Technical Report (711.68 KB)
Fiaschi, L, Nair, R, Köthe, U and Hamprecht, F A (2012). Learning to Count with Regression Forest and Structured Labels. ICPR 2012. Proceedings. 2685-2688PDF icon Technical Report (3.66 MB)
Feistner, L (2004). Statistische Karten In Der Magnetresonanzspektroskopie. University of Heidelberg
E
Eisele, H (2002). Automated defect detection and evaluation in X-ray CT images. University of Heidelberg. www.ub.uni-heidelberg.de/archiv/3106
Eisele, H and Hamprecht, F A (2003). A new approach for defect detection in X-ray CT images. Pattern Recognition. Springer. 2449 345-352PDF icon Technical Report (398.88 KB)
Eigenstetter, A, Takami, M and Ommer, B (2014). Randomized Max-Margin Compositions for Visual Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 3590--3597PDF icon Technical Report (8.01 MB)
Eigenstetter, A and Ommer, B (2012). Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity. Proceedings of the Conference on Advances in Neural Information Processing Systems. MIT Press. 377--385PDF icon Technical Report (3.27 MB)
Eigenstetter, A, Yarlagadda, P and Ommer, B (2012). Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching. Proceedins of the Aian Conference on Computer Vision. Springer. 152--163PDF icon Technical Report (7.31 MB)
D
Drory, A, Haubold, C, Avidan, S and Hamprecht, F A (2014). Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR. Proceedings. 43-53PDF icon Technical Report (2.6 MB)
Diego, F and Hamprecht, F A (2014). Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS. Proceedings. 64-72. http://papers.nips.cc/paper/5342-sparse-space-time-deconvolution-for-calcium-image-analysisPDF icon Technical Report (5.27 MB)
Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation for Identifying Neuronal Activity. Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of AbstractsPDF icon Technical Report (1.05 MB)
Diego, F and Hamprecht, F A (2013). Learning Multi-Level Sparse Representation. NIPS. Proceedings. http://papers.nips.cc/paper/5076-learning-multi-level-sparse-representationsPDF icon Technical Report (2.79 MB)
Diego, F, Reichinnek, S, Both, M and Hamprecht, F A (2013). Automated Identification of Neuronal Activity from Calcium Imaging by Sparse Dictionary Learning. ISBI 2013. Proceedings. 1058-1061PDF icon Technical Report (2.82 MB)
Diego, F, Serrat, J and López, A M (2012). Joint SpatioTemporal Alignment of Sequences. IEEE Transactions on Multimedia (TMM). PP (99) 1PDF icon Technical Report (5.15 MB)
Decker, C (2014). Automated Animal Behavior Classification. University of Heidelberg
Decker, C and Hamprecht, F A (2014). Detecting individual body parts improves mouse behavior classification. Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). ProceedingsPDF icon Technical Report (1.48 MB)
C
Carlsohn, M F, Menze, B H, Kelm, B Michael, Hamprecht, F A, Kercek, A, Leitner, R and Polder, G (2006). Color image processing. CRC Press. 7(17) 393-419
Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38
B
Börner, K, Hermle, J, Sommer, C, Brown, N P, Knapp, B, Glass, B, Torralba, G, Reymann, J, Beil, N, Beneke, J, Pepperkok, R, Schneider, R and Ludwig, T (2010). From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication. Biotechnology Journal. 5 39-49PDF icon Technical Report (556.09 KB)
Boppel, S (2008). Peak Identification For Liquid Chromatography And Mass Spectrometry. University of Heidelberg
Blumenthal, F (2014). Information-Geometric Optimization For Image Segmentation. University of Heidelberg
Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32PDF icon Technical Report (364.01 KB)
Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810PDF icon Technical Report (4.42 MB)
Bell, P and Ommer, B (2015). Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte. 68 414--420
Bell, P, Schlecht, J and Ommer, B (2013). Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History. Visual Resources Journal, Special Issue on Digital Art History. Taylor & Francis. 29 26--37. http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111
Beier, T, Kröger, T, Kappes, J H, Köthe, U and Hamprecht, F A (2014). Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning. 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014. http://dx.doi.org/10.1109/CVPR.2014.17PDF icon Technical Report (10.06 MB)
Beier, T (2014). Graph Based Image Analysis. University of Heidelberg
Beier, T, Hamprecht, F A and Kappes, J H (2015). Fusion Moves for Correlation Clustering. CVPR. Proceedings. 3507-3516PDF icon Technical Report (1.19 MB)
Bähnisch, C, Stelldinger, P and Köthe, U (2009). Fast and Accurate 3D Edge Detection for Surface Reconstruction. Pattern Recognition. Springer. 5748 111-120
A
Álvarez, J M, Gevers, T, Diego, F and López, A M (2012). Road Geometry Classification by Adaptive Shape Models. IEEE Transactions on Intelligent Transportation Systems (ITS). 99 1-10
Arnold, M, Bell, P and Ommer, B (2013). Automated Learning of Self-Similarity and Informative Structures in Architecture. Scientific Computing & Cultural Heritage
Antic, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. SpringerPDF icon Article (2.24 MB)

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