A spatial correlation based method for neighbor set selection in random field image models

Author

Khotanzad, Alireza ; Bennett, Jesse

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

Volume

8

Issue

5

fYear

1999

fDate

5/1/1999 12:00:00 AM

Firstpage

734

Lastpage

740

Abstract

Random field (RF) models have widespread application in image modeling and analysis. 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. We 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, and experimental results which demonstrate the viability of this method are presented

Keywords

Gaussian processes; Markov processes; autoregressive processes; correlation methods; image processing; random processes; AR model; Gauss-Markov random field model; autoregressive random field model; correlation structure; experimental results; image analysis; image modeling; neighbor set selection; neighbor sets; random field image models; simultaneous models; spatial correlation based method; spatial interactions; Conferences; Digital filters; Equations; Fuzzy reasoning; Fuzzy sets; Image processing; Random variables; Signal processing; Stochastic processes; Virtual manufacturing;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.760340

Filename

760340