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
	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
	LSBGalaxyDetect Feb 2005-Dec 2008 LSB galaxy detection using a Markov quadtree Keywords: detection, Markov quadtree, low surface brightness galaxies Project details Group participants: C. Collet [P.I.], M. Louys, M. Petremand, B. Perret, B. Perret
	MultiColorViz Oct 2003-Jun 2006 Color visualization of hyperspectral images Keywords: Multispectral, color display, HSV space, Fisher analysis, PCA, Markovian segmentation Project details Group participants: M. Petremand [P.I.], C. Collet, M. Louys
	HyperGalaxyClass Oct 2003-Nov 2006 Hyperspectral data cube analysis for galaxy classification Keywords: hyperspectral, galaxy classification, meanshift, projection, segmentation, reduction Project details Group participants: C. Collet [P.I.], B. Perret, M. Petremand, M. Louys
	FuzzyMarkovSegment Jan 2002-Dec 2010 Non-Stationary Fuzzy Markov Chain/Field Segmentation Keywords: Markov field, Markov chain, fuzzy models, fuzzy triplet Markov chain, non-stationary Markov chain Project details Group participants: F. Salzenstein [P.I.], C. Collet, M. Petremand
	QTreeMultiSegment Oct 1999-Jun 2004 Multiresolution Markov Modeling for Multichannel Segmentation Keywords: Unsupervised segmentation, Markovian quadtree, Generalized Gaussian model, multispectral data Project details Group participants: C. Collet [P.I.], M. Louys, M. Petremand

Journal papers and Book Chapters – peer-reviewed

• M. Petremand, M. Louys, C. Collet: “Color display for multiwavelength astronomical images” - Traitement du signal (TS), Numéro spécial, 21(6), Jan 2005
  • Abstract
  • BibTeX
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  • Project
  This paper proposes a new approach for the color display of multispectral/ hyperspectral images. The color representation of such data becomes problematic when the number of bands is higher than three, i.e. the basic RGB (Red, Green, Blue) representation is not straightforward. Here we employ a technique that uses a segmentation map, like an a priori information, and then compute a Factorial Discriminant Analysis (Fischer analysis) in order to allow, at best, a distribution of the information in the color space HSV (Hue, Saturation, Value). The information collected from the segmentation map (where each pixel is associated with class) has been shown to be advantages in the representation of the images through the results obtained on increasing size image collections in the framework of astronomical images. This method can easily be applied to other domains such as polarimetric or remote sensing imagery.
  @article{ref15,
  

  title = {Color display for multiwavelength astronomical images},
  journal = {Traitement du signal},
  author = {M. Petremand and M. Louys and C. Collet},
  volume = {21},
  number = {6},
  series = {Numéro spécial},
  url = {http://www.lis.inpg.fr/revue/},
  month = {Jan},
  year = {2005}
  

  }
• F. Salzenstein, C. Collet, M. Petremand: “Fuzzy Markov Random Fields for multispectral images” - Traitement du signal (TS), 21(1), Jan 2004
  • Abstract
  • BibTeX
  • PDF
  • Project
  This paper deals with a new statistical segmentation based on fuzzy multispectral markovian random fields model.We propose to solve the problem of parameter estimation, applying a stochastic gradient algorithm and empirical moment method, in order to estimate respectively the a priori parameters of the hidden Markovian field and the conditional densities of the observed data. Under correlated spectral band assumption, we introduce a model to express the variance-covariance matrix related to the fuzzy classes, by means of the ones related to the hard classes. We compare the results applying MPM (Mode of Posterior Marginales) and ICM (Iterated Conditional Mode) algorithms. We validate our procedure on synthetic images and test this approach on real multispectral astronomical data.
  @article{ref17,
  

  title = {Fuzzy Markov Random Fields for multispectral images},
  journal = {Traitement du signal},
  author = {F. Salzenstein and C. Collet and M. Petremand},
  volume = {21},
  number = {1},
  url = {http://www.lis.inpg.fr/revue/},
  month = {Jan},
  year = {2004}
  

  }