PMID- 18262883
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20100628
LR  - 20080211
IS  - 1057-7149 (Print)
IS  - 1057-7149 (Linking)
VI  - 8
IP  - 3
DP  - 1999
TI  - Segmentation of textured images using a multiresolution Gaussian autoregressive 
      model.
PG  - 408-20
LID - 10.1109/83.748895 [doi]
AB  - We present a new algorithm for segmentation of textured images using a 
      multiresolution Bayesian approach. The new algorithm uses a multiresolution 
      Gaussian autoregressive (MGAR) model for the pyramid representation of the 
      observed image, and assumes a multiscale Markov random field model for the class 
      label pyramid. The models used in this paper incorporate correlations between 
      different levels of both the observed image pyramid and the class label pyramid. 
      The criterion used for segmentation is the minimization of the expected value of 
      the number of misclassified nodes in the multiresolution lattice. The estimate 
      which satisfies this criterion is referred to as the "multiresolution 
      maximization of the posterior marginals" (MMPM) estimate, and is a natural 
      extension of the single-resolution "maximization of the posterior marginals" 
      (MPM) estimate. Previous multiresolution segmentation techniques have been based 
      on the maximum a posterior (MAP) estimation criterion, which has been shown to be 
      less appropriate for segmentation than the MPM criterion. It is assumed that the 
      number of distinct textures in the observed image is known. The parameters of the 
      MGAR model-the means, prediction coefficients, and prediction error variances of 
      the different textures-are unknown. A modified version of the 
      expectation-maximization (EM) algorithm is used to estimate these parameters. The 
      parameters of the Gibbs distribution for the label pyramid are assumed to be 
      known. Experimental results demonstrating the performance of the algorithm are 
      presented.
FAU - Comer, M L
AU  - Comer ML
AD  - Thomson Consumer Electronics, Indianapolis, IN 47907, USA.
FAU - Delp, E J
AU  - Delp EJ
LA  - eng
PT  - Journal Article
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
EDAT- 2008/02/12 09:00
MHDA- 2008/02/12 09:01
CRDT- 2008/02/12 09:00
PHST- 2008/02/12 09:00 [pubmed]
PHST- 2008/02/12 09:01 [medline]
PHST- 2008/02/12 09:00 [entrez]
AID - 10.1109/83.748895 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 1999;8(3):408-20. doi: 10.1109/83.748895.