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Krishnamoorthy Sivakumar, John Goutsias, "Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 2, pp. 99113, February, 1999.  
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@article{ 10.1109/34.748817, author = {Krishnamoorthy Sivakumar and John Goutsias}, title = {Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {21}, number = {2}, issn = {01628828}, year = {1999}, pages = {99113}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.748817}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis IS  2 SN  01628828 SP99 EP113 EPD  99113 A1  Krishnamoorthy Sivakumar, A1  John Goutsias, PY  1999 KW  Gibbs random fields KW  mathematical morphology KW  Metropolis algorithm KW  Monte Carlo simulation KW  morphological constraints KW  size density KW  statistical inference KW  texture synthesis and analysis. VL  21 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—A new class of Gibbs random fields (GRFs) is proposed capable of modeling geometrical constraints in images by means of mathematical morphology. The proposed models, known as morphologically constrained GRFs, model images by means of their size density. Since the size density is a multiresolution statistical summary, morphologically constrained GRFs explicitly incorporate multiresolution information into image modeling. Important properties are studied and their implication to texture synthesis and analysis is discussed. For morphologically constrained GRFs to be useful in practice, it is important that an efficient technique is available for fitting these models to real data. It is shown that, at low enough temperatures and under a natural condition, the maximumlikelihood estimator of the morphologically constrained GRF parameters can be approximated by means of an important tool of mathematical morphology known as the pattern spectrum. Therefore, statistical inference can be easily implemented by means of mathematical morphology. This allows the design of a computationally simple morphological Bayes classifier which produces excellent results in classifying natural textures.
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