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Fred A. Hamprecht

Fred A. Hamprecht Robert-Bosch endowed Full Professor for Multidimensional Image Processing

Interdisciplinary Center for Scientific Computing (IWR) and
Department of Physics and Astronomy
Heidelberg Collaboratory for Image Processing (HCI)
Speyerer Straße 6; D-69115 Heidelberg
Tel: ++49-6221-54 88 00; Assistant B. Werner: ++49-6221-54 88 75
fred.hamprecht@iwr.uni-heidelberg.de

Next office hours:
28.05.2013 14:00
04.06.2013 14:00
11.06.2013 14:00
18.06.2013 14:00

Research / Scientific Interests

• Learning (active, weakly supervised, structured) for automated image analysis
• Bioimage analysis
• Machine learning for everyone
• Segmentation in the neuro sciences
• Hobby project: What social network operators may know about non-members. Article, Press Coverage: NDR radio, Spiegel online, DRadio, New Scientist magazine, many others (pdf)

Publications

• Full list
• Selected recent
  • Active Learning with Distributional Estimates J. Röder, K. Kunzmann, B. Nadler, F. A. Hamprecht
    UAI 2012. Proceedings, in press, (2012) [Technical Report]
  • The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models B. Andres, B. H. Kappes, T. Beier, U. Köthe, F. A. Hamprecht
    ECCV 2012. Proceedings, in press, (2012) [Technical Report]
  • A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness B. X. Kausler, M. Schiegg, B. Andres, M. Lindner, U. Köthe, H. Leitte, J. Wittbrodt, L. Hufnagel, F. A. Hamprecht
    ECCV 2012. Proceedings, in press, (2012) [Technical Report]
  • Globally Optimal Closed-Surface Segmentation for Connectomics B. Andres, T. Kröger, K. L. Briggmann, W. Denk, N. Norogod, G. Knott, U. Köthe, F. A. Hamprecht
    ECCV 2012. Proceedings, in press, (2012) [Technical Report]
  • Structured Learning from Partial Annotations X. Lou, F. A. Hamprecht
    ICML 2012. Proceedings, in press, (2012) [Technical Report]
  • Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data J. Funke, B. Andres, F. A. Hamprecht, A. Cardona, M. Cook
    CVPR 2012. Proceedings, (2012) [Technical Report]
  • Seeded watershed cut uncertainty estimators for guided interactive segmentation C. Straehle, U. Köthe K. Briggman, W. Denk, F.A. Hamprecht
    CVPR 2012. Proceedings, in press, (2012) [Technical Report]
  • Learning to Segment Dense Cell Nuclei with Shape Prior X. Lou, F. A. Hamprecht
    CVPR 2012. Proceedings, in press, (2012) [Technical Report]
  • One plus one makes three (for social networks) E.-A. Horvath, M. Hanselmann, F.A. Hamprecht, K.A. Zweig
    PLoS ONE, in press, (2012) [doi:10.1371/journal.pone.0034740]
  • Structured Learning for Cell Tracking X. Lou, F. A. Hamprecht
    NIPS 2011. [Technical Report]
  • Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images A. Kreshuk, C. N. Straehle, C. Sommer, U. Köthe, M. Cantoni, G. Knott, F. A. Hamprecht
    PLoS ONE, (2011) 6 (10) [10.1371/journal.pone.0024899]
  • Probabilistic Image Segmentation with Closedness Constraints B. Andres, J. H. Kappes, T. Beier, U. Köthe, F. A. Hamprecht
    ICCV 2011 [10.1109/ICCV.2011.6126550 | Technical Report]
  • Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images Microscopy Images C. N. Straehle, U. Köthe, G. Knott, F. A. Hamprecht
    MICCAI 2011, 6891, 653-660 [10.1007/978-3-642-23623-5_82 | Technical Report]
  • On oblique random forests B. Menze, B. H. Kelm, N. Splitthoff, U. Köthe, F. A. Hamprecht
    ECML-PKDD 2011, 453-469 [10.1007/978-3-642-23783-6_29 | Technical Report]
  • SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists M. Hanselmann, B. Voss, B. Y. Renard, M. Lindner, U. Köthe, M. Kirchner, F. A. Hamprecht
    Bioinformatics, (2011) 27 (7), 987-993 [10.1093/bioinformatics/btr051 | Technical Report]
• Some old favorites
  • Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification B. Andres, U. Köthe, M. Helmstaedter, W. Denk, F. A. Hamprecht in: Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings, Springer(2008) 5096, 142-152 [10.1007/978-3-540-69321-5_15 | Technical Report]
  • NITPICK: Peak Identification for Mass Spectrometry Data B. Y. Renard, M. Kirchner, H. Steen, J. A J. Steen, F. A. Hamprecht BMC Bioinformatics, (2008) 9, 355 [10.1186/1471-2105-9-355]
  • Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification B. M. Kelm, B. H. Menze, C. M. Zechmann, K. T. Baudendistel, F. A. Hamprecht Magnetic Resonance in Medicine, (2007) 57, 150-159 [10.1002/mrm.21112 | Technical Report]
  • Optimal lattices for sampling H. R. Künsch, E. Agrell, F. A. Hamprecht IEEE Transactions on Information Theory, (2005) 51, 634-647 [10.1109/TIT.2004.840864]
  • Development and assessment of new exchange-correlation functionals F. A. Hamprecht, A. J. Cohen, D. J. Tozer, N. C. Handy Journal of Chemical Physics, (1998) 109, 6264-6271 [10.1063/1.477267]

Teaching

An Invitation to Image Analysis and Pattern Recognition Part 1 of 6 of a script I am writing (current version: 26.09.2010)

• Current courses: summer semester 2013
• Link to the LSF
• Vorlesung Image analysis
• Master-Pflichtseminar Computer Vision
• Last taught in 2012: Pattern Recognition
• Last taught in 2009: Imaging physics / Physik der Bildgebung

Pattern Recognition Video Lectures (Summer Term 2012). Full playlist here.

1 Introduction
• 1.1 Applications of Pattern Recognition
• 1.2 k-Nearest Neighbors Classification
• 1.3 Probability Theory
• 1.4 Statistical Decision Theory
2 Correlation measures, Gaussian Models
• 2.1 Pearson Correlation
• 2.2 Alternative Correl. Measures
• 2.3 Gaussian Graphical Models
• 2.4 Discriminant Analysis
3 Dimensionality Reduction
• 3.1 Regularized LDA/QDA
• 3.2 Principal Component Analysis (PCA)
• 3.3 Bilinear Decompositions
4 Neural Networks
• 4.1 History of Neural Networks
• 4.2 Perceptrons
• 4.3 Multilayer Perceptrons
• 4.4 The Projection Trick
• 4.5 Radial Basis Function Networks
5 Support Vector Machines
• 5.1 Loss Functions
• 5.2 Linear Soft-Margin SVM
• 5.3 Nonlinear SVM
6 Kernels, Random Forest
• 6.1 Kernels
• 6.2 One Class SVM
• 6.3 Random Forest
• 6.4 Random Forest Feature Importance
7 Regression
• 7.1 Least-Squares Regression
• 7.2 Optimum Experimental Design
• 7.3 Case Study: Functional MRI
• 7.4 Case Study: CT
• 7.5 Regularized Regression
8 Gaussian Processes
• 8.1 Gaussian Process Regression
• 8.2 GP Regression: Interpretation
• 8.3 Gaussian Stochastic Processes
• 8.4 Covariance Function
9 Unsupervised Learning
• 9.1 Kernel Density Estimation
• 9.2 Cluster Analysis
• 9.3 Expectation Maximization
• 9.4 Gaussian Mixture Models
10 Directed Graphical Models
• 10.1 Bayesian Networks
• 10.2 Variable Elimination
• 10.3 Message Passing
• 10.4 State Space Models
11 Optimization
• 11.1 The Lagrangian Method
• 11.2 Constraint Qualifications
• 11.3 Linear Programming
• 11.4 The Simplex Algorithm
12 Structured Learning
• 12.1 StructSVM
• 12.2 Cutting Planes