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Fred A. Hamprecht

Fred A. Hamprecht Robert-Bosch endowed Full Professor for Multidimensional Image Processing Fellow of the Marsilius Kolleg, class of 2010-2011

Interdisciplinary Center for Scientific Computing (IWR) and
Department of Physics and Astronomy

Office G.2.01
Heidelberg Collaboratory for Image Processing (HCI)
Speyerer Straße 6
D-69115 Heidelberg
Tel: ++49-6221-54 88 00
Sec: ++49-6221-54 88 75
Fax: ++49-6221-54 52 76
fred.hamprecht@iwr.uni-heidelberg.de

Next office hours:
22.05.2012 14:00

Research / Scientific Interests

• Learning (active, weakly supervised, structured) for automated image analysis in multiple dimensions
• Applications in industry and the life sciences
• General-purpose segmentation
• Segmentation in the neuro sciences
• Automated quality control

Publications

• Full list
• Selected recent
  • Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data J. Funke, B. Andres, F. A. Hamprecht, A. Cardona, M. Cook.
    CVPR 2012.
  • Seeded watershed cut uncertainty estimators for guided interactive segmentation C. Straehle, U. Koethe, K. Briggman, W. Denk, F.A. Hamprecht. CVPR 2012.
  • Learning to Segment Dense Cell Nuclei with Shape Prior X. Lou, F. A. Hamprecht. CVPR 2012.
  • Structured Learning for Cell Tracking X. Lou, F. A. Hamprecht in: 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 in: 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 in: 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 in: 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]
  • ilastik: Interactive Learning and Segmentation Toolkit C. Sommer, C. Strähle, U. Köthe, F. A. Hamprecht in: Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings, (2011), 230-233 [10.1109/ISBI.2011.5872394 | 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
  • 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

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
• Last taught in 2011: Pattern Recognition / Mustererkennung Also available on YouTube
• Last taught in 2009: Imaging physics / Physik der Bildgebung
• Last taught in 2008: Image Processing / Bildverarbeitung

Some Old Lecture Notes

Nowadays I prefer to deliver lectures on the blackboard. This collection of old slides does not reflect the content of the current lectures.

• Introduction to Statistics
• Signal processing
  • (pdf slides 2 up) (pdf slides 4 up) Introduction to signal processing, Fourier transform
  • (pdf slides 2 up) (pdf slides 4 up) Time-invariant systems, z-transform, filters
  • (pdf slides 2 up) (pdf slides 4 up) B-spline signal processing
  • (pdf slides 2 up) (pdf slides 4 up) Parametric spectral density estimation
  • (pdf slides 2 up) (pdf slides 4 up) Nonparametric spectral density estimation
  • (pdf slides 4 up) Hilbert transform (courtesy of M. Hissmann)
  • (pdf slides 2 up) (pdf slides 4 up) Time-frequency decompositions
  • (pdf slides 2 up) (pdf slides 4 up) Continuous wavelet transform
  • (pdf slides 2 up) (pdf slides 4 up) Multiresolution analysis
  • (pdf slides 2 up) (pdf slides 4 up) Discrete wavelet implementation and applications
  • (pdf slides 2 up) (pdf slides 4 up) Hidden Markov Models
  • (pdf slides 2 up) (pdf slides 4 up) Optimal discrete filters
  • (pdf slides 2 up) (pdf slides 4 up) Adaptive filters
  • (pdf slides 2 up) (pdf slides 4 up) Sampling and interpolation

• Image Processing
• (pdf slides 2 up) (pdf slides 4 up) Introduction to image processing; color spaces; image formats: TIFF, GIF, PNG
• (pdf slides 2 up) (pdf slides 4 up) Linear operators; discrete Fourier transform; discrete cosine transform; JPEG compression
• (pdf slides 2 up) (pdf slides 4 up) Filters and their optimization
• (pdf slides 2 up) (pdf slides 4 up) Linear isotropic diffusion filtering; Gaussian and Laplacian pyramid and their efficient implementation
• (pdf slides 2 up) (pdf slides 4 up) Anisotropic (Perona-Malik) diffusion filtering; structure tensor and coherence-enhancing diffusion
• (pdf slides 2 up) (pdf slides 4 up) Bayesian image analysis; Markov and Gibbs random fields; Markov Chain Monte Carlo methods
• (pdf slides 2 up) (pdf slides 4 up) Gibbs sampler, Metropolis sampler, minimization heuristics: iterated conditional modes (ICM), relaxation labeling (RL), highest confidence first (HCF), graduated non-convexity (gnc)
• (pdf slides 2 up) (pdf slides 4 up) Applications of Bayesian image analysis: smoothing, restoration and deconvolution; object matching; spin glass Markov random fields in object recognition
• (pdf slides 2 up) (pdf slides 4 up) Texture description and recognition: filter banks and textons, the role of vector quantization, joint distribution of intensities, Haralick and Unser parameters
• Pattern recognition
  • (pdf slides 2 up) (pdf slides 4 up) Introduction to pattern recognition; linear and quadratic discriminant analysis (LDA, QDA)
  • (pdf slides 2 up) (pdf slides 4 up) Statistical decision theory; nearest neighbor methods
  • (pdf slides 2 up) (pdf slides 4 up) Model selection and assessment: bias-variance trade-off, resampling methods: cross-validation, bootstrap
  • (pdf slides 2 up) (pdf slides 4 up) Perceptron and multilayer-perceptron (feed-forward neural networks)
  • (pdf slides 2 up) (pdf slides 4 up) Support Vector Machines
  • Ordinary least squares, Regularized Regression Methods
  • Total least squares / errors-in-variables
  • Outlier diagnostics and robust regression
  • (pdf slides 2 up) (pdf slides 4 up) Kernel density estimation
  • (pdf slides 2 up) (pdf slides 4 up) Cluster analysis
  • Multidimensional Scaling
  • Nonlinear dimension reduction
  • (pdf slides 2 up) (pdf slides 4 up) Local linear embedding; bilinear decomposition

Last update: 03.05.2012, 16:51

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