Parsimonious Segmentation of Time Series by Potts Models
Typical problems in the analysis of data sets like time-series or images crucially rely on the extraction of primitive features based on segmentation. Variational approaches are a popular and convenient framework in which such problems can be studied. We focus on Potts models as simple nontrivial instances. The discussion proceeds along two data sets from brain mapping and functional genomics.
KeywordsPotts Model Perfect Match Fractionation Curve Classical Criterion Primitive Feature
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- BLAKE, A. and ZISSERMAN, A. (1987): Visual Reconstruction. The MIT Press Series in Artificial Intelligence, MIT Press, Massachusetts, USA.Google Scholar
- DROBYSHEV, A.L, MACHKA, CHR., HORSCH, M., SELTMANN, M., LIEB-SCHER, V, HRABÉ DE ANGELIS, V., and BECKERS, J. (2003): Specificity assessment from fractionation experiments, (SAFE): a novel method to evaluate microarray probe specificity based on hybridization stringencies. Nucleic Acids Res., 31(2), 1–10.CrossRefGoogle Scholar
- FRIEDRICH, F. (2003a): Stochastic Simulation and Bayesian Inference for Gibbs fields. CD-ROM, Springer Verlag, Heidelberg, New York.Google Scholar
- FRIEDRICH, F. (2003b): AntsInFields: Stochastic simulation and Bayesian inference for Gibbs fields, URL: http://www.AntsInFields.de.Google Scholar
- KEMPE, A. (2003): Statistical analysis of the Potts model and applications in biomedical imaging. Thesis, Institute of Biomathematics and Biometry, National Research Center for Environment and Health Munich, Germany.Google Scholar
- SERRA, J. (1982, 1988): Image analysis and mathematical morphology. Vol. I, II. Acad. Press, London.Google Scholar
- WINKLER, G. (2003): Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction. volume 27 of Applications of Mathematics, Springer Verlag, Berlin, Heidelberg, New York, second edition.Google Scholar