Publications
2004
Hamprecht, F A (2004).
Classification.
Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press. 509-519
Technical Report (320.84 KB) Menze, B H, Wormit, M, Bachert, P, Lichy, M P, Schlemmer, H - P and Hamprecht, F A (2004).
Classification of in vivo magnetic resonance spectra.
Classification in ubiquitous challenge: Proceedings of the GfKl 2004. Springer. 362-369
Technical Report (240.1 KB) 2006
Zechmann, C M, Kelm, B Michael, Zamecnik, P, Ikinger, U, Waldherr, R, Röll, S, Delorme, S, Hamprecht, F A and Bachert, P (2006).
Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients.
Proceedings of the 16th ISMRM Technical Report (664.38 KB) Kelm, B Michael, Menze, B H, Neff, T, Zechmann, C M and Hamprecht, F A (2006).
CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods..
Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen. Springer. 51-55.
http://www.efmi-wg-mip.net/service/bvm2006 Technical Report (275.25 KB) 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
2007
Weber, C, Zechmann, C M, Kelm, B Michael, Zamecnik, R, Hendricks, D, Waldherr, R, Hamprecht, F A, Delorme, S, Bachert, P and Ikinger, U (2007).
Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer.
Der Urologe.
46 1252
2008
Hanselmann, M, Kirchner, M, Renard, B Y, Amstalden, E R, Glunde, K, Heeren, R M A and Hamprecht, F A (2008).
Concise Representation of MS Images by Probabilistic Latent Semantic Analysis.
Analytical Chemistry.
80 9649-9658
Technical Report (3.91 MB) 2009
Kaster, F O, Kelm, B Michael, Zechmann, C M, Weber, M - A, Hamprecht, F A and Nix, O (2009).
Classification of Spectroscopic Images in the DIROlab Environment.
World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany. Springer.
25/V 252--255
Technical Report (145.73 KB) Menze, B H, Kelm, B Michael, Masuch, R, Himmelreich, U, Bachert, P, Petrich, W and Hamprecht, F A (2009).
A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data.
BMC Bioinformatics.
10:213 Technical Report (675 KB) 2010
Kirchner, M, Renard, B Y, Köthe, U, Pappin, D J, Hamprecht, F A, Steen, J A J and Steen, H (2010).
Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments.
Bioinformatics.
26 (1) 77-83
Technical Report (380.19 KB) 2013
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013).
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems.
CVPR 2013. Proceedings Technical Report (1.35 MB) Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013).
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem.
CVPR Technical Report (1.35 MB) Maco, B, Holtmaat, A, Cantoni, M, Kreshuk, A, Straehle, C N, Hamprecht, F A and Knott, G W (2013).
Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons.
PloS one.
8 (2) Technical Report (2.13 MB) 2014
Kappes, J H, 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 (2014).
A Comparative Study of Modern Inference Techniques for Structured
Discrete Energy Minimization Problems.
CoRR.
http://arxiv.org/abs/1404.0533 Kappes, J H, 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 (2014).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
CoRR.
abs/1404.0533.
http://hci.iwr.uni-heidelberg.de/opengm2/ Technical Report (3.32 MB) 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.2014.17 Technical Report (10.06 MB) 2015
Kappes, J H, 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 (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
Int.~J.~Comp.~Vision Technical Report (5.12 MB) Kappes, J H, 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 (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision. 1-30
Technical Report (1.5 MB) Kappes, J H, 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 (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184.
http://hci.iwr.uni-heidelberg.de/opengm2/ Kappes, J H, 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 (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184
Kappes, J H, 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 (2015).
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems.
International Journal of Computer Vision.
115 155–184