Astronomy & Astrophysics

	CubeCombinaisonFusion Jan 2009-Jan 2013 Hyperspectral data fusion Keywords: optimal data fusion, spatial and spectral super-resolution, model-based, recursive update, Bayesian inference Project details Group participants: C. Collet [P.I.], M. Petremand, F. Salzenstein, V. Mazet
	CubeDeconvolution Jan 2009-Jan 2013 Deconvolution of hyperspectral observations with varying PSF Keywords: deconvolution, varying PSF, astronomical data cube Project details Group participants: V. Mazet, C. Collet
	CubeSourceSeparation Jan 2009-Jan 2013 Source separation in hyperspectral deep field observations Keywords: source separation, decomposition into elementary signals, astronomical data cube Project details Group participants: C. Collet, V. Mazet, F. Salzenstein, M. Louys
	CubeVisualization Jan 2009-Jan 2013 Development of a visualization tool enabling large IFU data cubes upload, navigation and interpretation Keywords: Visualization, massive data sets, hyperspectral data, IFU Project details Group participants: M. Louys [P.I.], M. Petremand, V. Mazet, C. Collet
	GalaxyClassification Oct 2007-Oct 2010 Galaxy classification using both spatial (morphological) and spectral aspects Keywords: galaxy classification, multivariate cube, decomposition into structures, Bayesian inference, mathematical morphology Project details Group participants: C. Collet [P.I.], V. Mazet, B. Perret
	AstroCubeSegmentation Sep 2006-Dec 2009 Hyperspectral data cube segmentation of astronomical images Keywords: Data cube, spectral line estimation, mixture of Gaussians, decomposition into elementary patterns, MCMC Project details Group participants: V. Mazet [P.I.], C. Collet

Abstracts, Posters, Preprints, Reports and Theses

• V. Mazet: “Développement de méthodes de traitement de signaux spectroscopiques : estimation de la ligne de base et du spectre de raies” - University of Nancy 1 PhD Thesis, France, Dec 2005
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  This thesis is part of a collaboration between the CRAN (UMR 7039) and the LCPME (UMR 7564) aiming at developing analysis methods for spectroscopic signals.
  Firstly, a deterministic method is proposed. It allows to estimate the baseline as the polynom minimizing a non-quadratic cost function (Huber function, truncated quadratic). In particular, asymmetrical cost functions are well adapted for spectra whose peaks are positive. The minimization is achieved by the half-quadratic minimisation algorithm LEGEND.
  Secondly, we consider the problem of the spectral peak estimation: the Bayesian approach coupled with MCMC methods provides a very powerful theoretical framework. In a first approach, the problem is considered as an unsupervised blind positive sparse spike deconvolution. The sparse spike train is modelled as a Bernoulli-Gaussian process with positive support; an original mixed accept-reject algorithm allows the simulation of positive normal variables. An interesting alternative method consists in considering the problem as a decomposition into elementary signals. An original model is introduced, allowing to keep the model dimension fixed. The label switching problem is also studied and a relabelling algorithm is proposed.
  The methods are applied to both simulated and experimental spectra (infrared and Raman).
  @phdthesis{ref63,
  

  title = {Développement de méthodes de traitement de signaux spectroscopiques : estimation de la ligne de base et du spectre de raies},
  school = {University of Nancy 1},
  url = {http://www.uhp-nancy.fr},
  author = {V. Mazet},
  address = {France},
  month = {Dec},
  year = {2005}
  

  }