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Titolo:
Characterization of stationary images by non Gaussian bidimensional movingaverage models
Autore:
Bakrim, M; Aboutajdine, D;
Indirizzi:
Fac Sci & Tech, Dept Phys, Marrakech, Morocco Fac Sci & Tech Marrakech Morocco & Tech, Dept Phys, Marrakech, Morocco Fac Sci Rabat, GSC, LEESA, Dept Phys, Rabat, Morocco Fac Sci Rabat RabatMorocco abat, GSC, LEESA, Dept Phys, Rabat, Morocco
Titolo Testata:
ANNALES DES TELECOMMUNICATIONS-ANNALS OF TELECOMMUNICATIONS
fascicolo: 9-10, volume: 56, anno: 2001,
pagine: 523 - 537
SICI:
0003-4347(200109/10)56:9-10<523:COSIBN>2.0.ZU;2-9
Fonte:
ISI
Lingua:
FRE
Soggetto:
HIGHER-ORDER STATISTICS; FIR SYSTEM-IDENTIFICATION; NONMINIMUM-PHASE SYSTEMS; PARAMETER-ESTIMATION; RANDOM-FIELDS; TEXTURE SYNTHESIS; ARMA MODELS; CUMULANT; BISPECTRUM; MA;
Keywords:
image processing; bidimensional signal; stationary signal; non Gaussian signal; random signal; statistical model; auto-correlation; cumulant; least square method; linear model;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
51
Recensione:
Indirizzi per estratti:
Indirizzo: Bakrim, M Fac Sci & Tech, Dept Phys, BP 618, Marrakech, Morocco Fac Sci & Tech BP 618 Marrakech Morocco 18, Marrakech, Morocco
Citazione:
M. Bakrim e D. Aboutajdine, "Characterization of stationary images by non Gaussian bidimensional movingaverage models", ANN TELECOM, 56(9-10), 2001, pp. 523-537

Abstract

In this paper, four batch least squares linear approaches are presented for identification of non minimum phase bidimensional non Gaussian moving average (MA) models, and a relationship between autocorrelation and cumulant sequences is given. One of the proposed methods is cumulant-based only but the others use both autocorrelations and in-th order cimulants (m. > 2). Three of them are derived from the BrillingerRoseliblatt's non linear relationby using the Tugnait's closed-form solution. Also, we generalize to m-th order cumulants the 2-D version of Giannakis-Mendel's approach. By simulations, we test and compare the Tugnait's closed-form solution and the proposedmethods, and we evaluate the performance of our relationship in noisy environment. Finally we propose to characterize textured images by a 2-D MA model witch will be identified using our approaches in noisy, and free noise cases.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 05/04/20 alle ore 19:30:46