Cross, G. R. and Jain, A. K., Markov Random Field Texture
Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5, 25-39,
1983.
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| Abstract: We consider a texture to be a stochastic,
possibly periodic, two-dimensional image field. A texture model is a mathematical
procedure capable of producing and describing a textured image. We explore the use of
Markov random fields as texture models. The binomial model, where each point in the
texture has a binomial distribution with parameter controlled by its neighbors and
"number of tries" equal to the number of gray levels, was taken to be the basic
model for the analysis. A method of generating samples from the binomial model is given,
followed by a theoretical and practical analysis of the method's convergence. Examples
show how the parameters of the Markov random field control the strength and direction of
the clustering in the image. The power of the binomial model to produce blurry, sharp,
line-like, and blob-like textures is demonstrated. Natural texture samples were digitized
and their parameters were estimated under the Markov random field model. A hypothesis test
was used for an objective assessment of goodness-of-fit under the Markov random field
model. Overall, microtextures fit the model well. The estimated parameters of the natural
textures were used as input to the generation procedure. The synthetic microtextures
closely resembled their real counterparts, while the regular and inhomogeneous textures
did not. |