Title :
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
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;
Conference_Titel :
Image Processing, 2005. ICIP 2005. IEEE International Conference on
Print_ISBN :
0-7803-9134-9
DOI :
10.1109/ICIP.2005.1530598
