AN UNSUPERVISED IMAGE SEGMENTATION USING POISSON MARKOV RANDOM FIELDS

Abdülkadir ŞENGÜR¹

Markov random field (MRF)-based image segmentation methods have gained considerable interest over the last few decades. The ubiquitous of MRF is the conditional model that has a joint Gaussian distribution so it is called Gaussian MRF (GMRF). On the other hand, Pal and Pal (1991), proposed that image histograms were more appropriately modeled by the mixture of Poisson distributions. Therefore, in this paper, we proposed a simple unsupervised Poisson MRF (PMRF) for gray level image segmentation. The proposed PMRF has been tested on a variety of images including artificial images and real world images. Experimental results show that by visually and by numerically comparing, it is obvious that using PMRF model generates much more accurate results than the GMRF.

Keywords
Unsupervised Image Segmentation,, Poisson Distribu,