Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing

REFERENCES

• D M Goodman & M P Ekstrom, Multidimensional spectral factorization and univariate AR models, IEEE Trans Automat Control, vol AC-25, pp 258–262, 1980
   Web of Science ®Google Scholar
• J E Besag, Spatial interaction and statistical analysis of lattice systems, J Royal Slat Soc Ser B, vol B-35 pp 192–236.
   Google Scholar
• Y A Rosanov, On Gaussian fields with given conditional distributions, Theory of Prob and its Applications, vol 12, pp 381–391, 1967.
   Web of Science ®Google Scholar
• J W Woods, Two-dimensional discrete Markovian random fields, IEEE Trans Information Theory, vol IT-18, pp 232–240, 1972.
   Web of Science ®Google Scholar
• G R Cross & A K Jain, Markov random field texture models, IEEE Trans Patt Anal and Mach Intel, vol PAMI-5, pp 25–40, 1983.
   Web of Science ®Google Scholar
• R L Kashyap, Analysis and synthesis of image patterns by Spatial Interaction Models, in Pattern Recognition, (L N Kanal and A Rosenfeld, eds.), North-Holland Publishing Company, 1981.
   Google Scholar
• R L Kashyap, Random field models on finite lattices for finitie images, Proc Conf on Information Sciences and Systems, Johns Hopkins University, pp 215–220, 1981.
   Google Scholar
• R Chellappa, Two-dimensional discrete Gaussian Markov random field models for image processing, in Progress in Pattern Recognition, (L N Kanal and A Rosenfeld eds.), North Holland Publishing Co, pp 79–112, 1985.
   Google Scholar
• R Chellappa & R L Kashyap, Digital image restoration using spatial interaction models, IEEE Trans on Acoust Speech and Signal Processing, vol ASSP-30, 461–472,1982.
   Google Scholar
• R Chellappa & R L Kashyap, Synthetic generation and estimation in random field models of images, Proc IEEE Computer Society Conf on Patt Recog and Image Processing, Dallas, Texas, pp 577–582, 1981.
   Google Scholar
• R Chellappa & S Chatterjee, Texture synthesis and coding using Gaussian Markov random field models, IEEE Trans Syst Man and Cybern, vol SMC-15, pp 298–303, March 1985.
   Web of Science ®Google Scholar
• R L Kashyap & R Chellappa, Estimation and choice of neighbours in spatial interaction models of images, IEEE Trans on Inform Theory, vol IT-20, pp 60–72, 1983.
   Google Scholar
• PAP Moran & J E Besag, On the estimation testing and of spatial interaction in Gaussian lattice, Biometrika, vol 62, pp 555–562, 1975.
   Web of Science ®Google Scholar
• G Sharma & R Caellappa, A model based approach for the estimation of maximum entropy spectra, IEEE Trans Inform Theory, vol IT-31, pp 90–99, 1985.
   Web of Science ®Google Scholar
• R Chellappa, Y H Hu & S Y Kung, On Two-dimensional Markov spectral estimation, IEEE Trans Acoust Speech and Signal Processing, ASSP-31, pp 836–841, 1983.
   Google Scholar
• R M Haralick, Statistical and structural approaches to texture, Proc IEEE, vol 67, pp 786–804, 1979.
   Web of Science ®Google Scholar
• R M Haralick, K Shanmugam & I Dinstein, Textual features for classification, IEEE Trans Syst Man ami Cybern, vol SMC-3, pp 610–621, 1973.
   Web of Science ®Google Scholar
• J Weszka, C R Dyer & A Rosenfeld, A comparative study of texture measures for terrian classification, IEEE Trans Syst Man Cybern, vol SMC-6, pp 269–285, 1976.
   Web of Science ®Google Scholar
• O D Faugeras et al, Decorrelation methods of texture feature extraction, IEEE Trans Patt Anal and Mach Intel, vol PAMI-2, pp 323–332, 1980.
   Web of Science ®Google Scholar
• R H Duda & P E Hart, Pattern Classification and Scene Analysis, Wiley Interscience, New York, 1973.
   Google Scholar
• R Chellappa & S Chatterjee, Texture classification using Gauss Markov random field models, IEEE Trans on Acoust Speech, Signal Processing, vol ASSP-33, pp 959–963, 1985.
   Google Scholar
• M HDeGroot, Optimal statistical decisions, McGraw-Hill, New York: 1970.
   Google Scholar
• C W Therrien, An estimation theoretic approach to terrain image segmentation, Comp Vision Graph, Image Proc, vol 22, pp 313–326, 1983.
   Google Scholar
• H Derin & W S Cole, Segmentation of textured images using gibbs random fields, Comp. Vision Graph Image Proc, vol 35, pp 72–98, 1986.
   Google Scholar
• F S Cohen & D B Cooper, Simple parallel hierarchical and relaxation aglorithms for segmenting noncausal Markovian random fields, IEEE Trans Patt Anal, and Mach Intel, vol PAM 1–9, pp 195–219, 1987.
   Web of Science ®Google Scholar
• B D Ripley, Statistics images and pattern recognition, Canad Jour Stat, vol 14, pp 83–111, 1986.
   Web of Science ®Google Scholar
• S Chatterjee & R Chellappa, Model based texture segmentation, Proc 20th Asilomar Conf on Signals, Systems and Computers, Monetary, CA, pp 235–239 CA, 1986.
   Google Scholar
• R Chellappa & S Chatterjee, Texture segmentation using Gaussian Markov random field models, Proc IEEE Computer Society Conf on Comp Vis and Patt Recog, San Francisco, CA, pp 215–217, 1985.
   Google Scholar
• S Geman & D Geman, Stochastic relaxation Gibbs' distributions and the Bayesian restoration of images, IEEE Trans Patt Anal and Mach Intel, vol PAMI-6, pp 721–741, 1984.
   Web of Science ®Google Scholar
• S Geman & D Graffigne, Markov random field image models and their application to computer vision, Proc of the Intel Congress of Mathematicians, Berkeley, CA, pp 1496–1517, 1987.
   Google Scholar
• R L Kashyap, Optimal features selection and decision rules in classification problems with time series, IEEE Trans Inform Theory, vol IT-17, 281–288, 1978.
   Google Scholar
• H C Andrews & B R Hunt, Digital Image Restoration, Prentice-Hall, New Jersey: 1977.
   Google Scholar
• A K Jain, A Fast Karhunen-Loeve transform for digital restoration of images degraded by white and colored noise, IEEE Trans Computers, vol C-26, pp 560–571, 1977.
   Web of Science ®Google Scholar
• M S Murphy & L M Silverman, Image model representation and line by line recursive restoration, IEEE Trans Automat Control. Vol AC-23, pp 808–816, 1978.
   Google Scholar
• J W Woods & C H Radewan, Kalman filtering in two dimensions, IEEE Trans Information Theory, vol IT-23, pp 473–482, 1977.
   Web of Science ®Google Scholar
• H Jinchi & R Chellappa, Restoration of blurred and noisy images using Gauss Markov random field models, Proc Conf on Inform Sciences und Systems, Princeton University, pp 34–39, 1986.
   Google Scholar
• R Chellappa, T Simchony & H Jinchi, Relaxation algorithms for the restoration of gray level images degraded by multiplicative noise, Conf Inform Sciences and Systems, Johns Hopkins University, pp 500–505, 1987.
   Google Scholar
• H Jinchi, Image restoration algorithms using Gauss Markov random field models, PhD Dissertation, University of Southern California, 1986.
   Google Scholar