A unified model of GMRF and MOG for image segmentation

Author

Peng, YU ; Xing-Wei, Tong ; Ju-Fu, FENG

Author_Institution

Sch. of Math. Sci., Peking Univ., China

Volume

3

fYear

2005

fDate

11-14 Sept. 2005

Abstract

In texture segmentation, features must be firstly extracted in the mixture-of-Gaussian (MOG) models. In this paper, we combine MOG model with Gauss Markov random field (GMRF) model and get a unification model. This unified model takes interaction coefficients of neighbor pixels as parameters. We derivate a set of parameters estimation equations by expectation-maximization (EM) algorithms and apply them to a two-class texture segmentation problem. Experimental results show the efficiencies and strengths of the model.

Keywords

Gaussian processes; Markov processes; expectation-maximisation algorithm; image segmentation; image texture; random processes; Gauss Markov random field; expectation-maximization algorithms; image segmentation; mixture-of-Gaussian models; texture segmentation; Density functional theory; Equations; Feature extraction; Gaussian processes; Image processing; Image segmentation; Markov random fields; Mathematical model; Parameter estimation; Probability density function; EM algorithm; Gauss Markov random field; Image Segmentation; Mixture-of-Gaussian; Textured Image;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2005. ICIP 2005. IEEE International Conference on

Print_ISBN

0-7803-9134-9

Type

conf

DOI

10.1109/ICIP.2005.1530598

Filename

1530598