Multispectral random field models for synthesis and analysis of color images

Author

Bennett, Jesse ; Khotanzad, Alireza

Author_Institution

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

Volume

20

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

327

Lastpage

332

Abstract

Multispectral extensions to the traditional gray level simultaneous autoregressive (SAR) and Markov random field (MRF) models are considered. Furthermore, a new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model, yet admits to a simplified parameter estimation method. These models are well-suited to analysis and modeling of color images. For each model considered, procedures are developed for parameter estimation and image synthesis. Experimental results, based on known image models and natural texture samples, substantiate the validity of thee results

Keywords

Markov processes; autoregressive processes; image colour analysis; image texture; least squares approximations; parameter estimation; color images; image analysis; image synthesis; multispectral random field models; pseudo-Markov model; Image analysis; Image coding; Image color analysis; Image generation; Image segmentation; Image texture analysis; Lattices; Markov random fields; Parameter estimation; Radio frequency;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.667889

Filename

667889