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Titolo:
A spatial correlation based method for neighbor set selection in random field image models
Autore:
Khotanzad, A; Bennett, J;
Indirizzi:
So Methodist Univ, Dept Elect Engn, Dallas, TX 75275 USA So Methodist Univ Dallas TX USA 75275 pt Elect Engn, Dallas, TX 75275 USA
Titolo Testata:
IEEE TRANSACTIONS ON IMAGE PROCESSING
fascicolo: 5, volume: 8, anno: 1999,
pagine: 734 - 740
SICI:
1057-7149(199905)8:5<734:ASCBMF>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Keywords:
automatic neighbor set selection; Gauss-Markov models; model identification; random field models; simultaneous autoregressive models; spatial interaction models; texture modeling;
Tipo documento:
Letter
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
17
Recensione:
Indirizzi per estratti:
Indirizzo: Khotanzad, A So Methodist Univ, Dept Elect Engn, Dallas, TX 75275 USA So Methodist Univ Dallas TX USA 75275 , Dallas, TX 75275 USA
Citazione:
A. Khotanzad e J. Bennett, "A spatial correlation based method for neighbor set selection in random field image models", IEEE IM PR, 8(5), 1999, pp. 734-740

Abstract

Random field (RF) models have widespread application in image modeling andanalysis. The effectiveness of these models is largely dependent on the choice of neighbor sets, which determine the spatial interactions that are representable by the model. In this work, me consider the problem of selecting these neighbor sets for simultaneous autoregressive and Gauss-Markov random field models, based on the correlation structure of the image to be modeled. A procedure for identifying appropriate neighbor sets is proposed, andexperimental results which demonstrate the viability of this method are presented.

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Documento generato il 23/01/21 alle ore 02:36:19