Consiglio Nazionale delle Ricerche

Tipo di prodottoContributo in atti di convegno
TitoloA minimax entropy method for blind separation of dependent components in astrophysical images
Anno di pubblicazione2006
FormatoElettronico
Autore/iCaiafa C. F., Kuruoglu E. E., Proto A. N.
Affiliazioni autoriLab. de Sistemas Complejos, Fac. de Ingeniería - UBA, Buenos Aires, Argentina; CNR-ISTI, Pisa, Italy; Comisión de Inv. Científicas de la Prov. de Buenos Aires, Argentina, Italy
Autori CNR e affiliazioni
  • ERCAN ENGIN KURUOGLU
Lingua/e
  • inglese
AbstractWe develop a new technique for blind separation of potentially non independent components in astrophysical images. Given a set of linearly mixed images, corresponding to different measurement channels, we estimate the original electromagnetic radiation sources in a blind fashion. Specifically, we investigate the separation of cosmic microwave background (CMB), thermal dust and galactic synchrotron emissions without imposing any assumption on the mixing matrix. In our approach, we use the Gaussian and non-Gaussian features of astrophysical sources and we assume that CMB-dust and CMB-synchrotron are uncorrelated pairs while dust and synchrotron are correlated which is in agreement with theory. These assumptions allow us to develop an algorithm which associates the Minimum Entropy solutions with the non-Gaussian sources (thermal dust and galactic synchrotron emissions) and the Maximum Entropy solution as the only Gaussian source which is the CMB. This new method is more appropriate than ICA algorithms because independence between sources is not imposed which is a more realistic situation. We investigate two specific measures associated with entropy: Gaussianity Measure (GM) and Shannon Entropy (SE) and we compare them. Finally, we present a complete set of examples of separation using these two measures validating our approach and showing that it performs better than FastICA algorithm. The experimental results presented here were performed on an image database that simulates the measurements expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a81
Pagine totali88
RivistaAIP conference proceedings
Attiva dal 1970
Editore: American Institute of Physics, - New York
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0094-243X
Titolo chiave: AIP conference proceedings
Titolo proprio: AIP conference proceedings.
Titolo abbreviato: AIP conf. proc.
Titoli alternativi:
  • A.I.P. conference proceedings
  • American Institute of Physics conference proceedings
Numero volume della rivista872
Serie/Collana-
Titolo del volume-
Numero volume della serie/collana-
Curatore/i del volumeA. M. Djafari
ISBN-
DOI-
Editore-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveSource separation, Dependent component analysis, Minimax entropy, Astrophysical microwave radiation images
Link (URL, URI)http://aip.scitation.org/doi/abs/10.1063/1.2423263
Titolo convegno/congressoBayesian Inference and Maximum Entropy Methods In Science and Engineering
Luogo convegno/congressoParis, France
Data/e convegno/congresso08-13/07/2006
RilevanzaInternazionale
RelazioneContributo
Titolo parallelo-
Note/Altre informazioniCodice puma: cnr.isti/2006-A2-75
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P10.012.001 : Elaborazione di segnali e immagini per impieghi diagnostici e interpretazione di immagini multisorgente
Progetti Europei-
Allegati
A minimax entropy method for blind separation of dependent components in astrophysical images (documento privato )
Tipo documento: application/pdf

Dati storici
I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
Area disciplinareComputer Science & Engineering
NoteIn: Bayesian Inference and Maximum Entropy Methods In Science and Engineering (Paris, France, July 8-13, 2006). Proceedings, pp. 81-88. A. M. Djafari (ed.). American Institute of Physics, 2006.
Descrizione sintetica del prodottoABSTRACT: We develop a new technique for blind separation of potentially non independent components in astrophysical images. Given a set of linearly mixed images, corresponding to different measurement channels, we estimate the original electromagnetic radiation sources in a blind fashion. Specifically, we investigate the separation of cosmic microwave background (CMB), thermal dust and galactic synchrotron emissions without imposing any assumption on the mixing matrix. In our approach, we use the Gaussian and non-Gaussian features of astrophysical sources and we assume that CMB-dust and CMB-synchrotron are uncorrelated pairs while dust and synchrotron are correlated which is in agreement with theory. These assumptions allow us to develop an algorithm which associates the Minimum Entropy solutions with the non-Gaussian sources (thermal dust and galactic synchrotron emissions) and the Maximum Entropy solution as the only Gaussian source which is the CMB. This new method is more appropriate than ICA algorithms because independence between sources is not imposed which is a more realistic situation. We investigate two specific measures associated with entropy: Gaussianity Measure (GM) and Shannon Entropy (SE) and we compare them. Finally, we present a complete set of examples of separation using these two measures validating our approach and showing that it performs better than FastICA algorithm. The experimental results presented here were performed on an image database that simulates the measurements expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.