@string{ivc="Image and Vision Computing"}
@string{elsevier="Elsevier"}
@Article{Kato-Pong2006a,
  author =	 {Kato, Zoltan and Pong, Ting Chuen},
  title =	 {A {M}arkov Random Field Image Segmentation Model for
                  Color Textured Images},
  journal =	 ivc,
  year =	 2006,
  volume =	 24,
  number =	 10,
  pages =	 {1103--1114},
  month =	 oct,
  publisher =	 elsevier,
  pdf =		 {papers/ivc2001.pdf},
  abstract =	 {We propose a Markov random field (MRF) image
                  segmentation model which aims at combining color and
                  texture features. The theoretical framework relies
                  on Bayesian estimation via combinatorial
                  optimization (Simulated Annealing). The segmentation
                  is obtained by classifying the pixels into different
                  pixel classes. These classes are represented by
                  multi-variate Gaussian distributions. Thus, the only
                  hypothesis about the nature of the features is that
                  an additive Gaussian noise model is suitable to
                  describe the feature distribution belonging to a
                  given class. Here, we use the perceptually uniform
                  CIE-L*u*v* color values as color features and a set
                  of Gabor filters as texture features. Gaussian
                  parameters are either computed using a training data
                  set or estimated from the input image. We also
                  propose a parameter estimation method using the EM
                  algorithm. Experimental results are provided to
                  illustrate the performance of our method on both
                  synthetic and natural color images.}
}