Image modelling markov random field
no vote
Image modeling plays an important role in
modern image processing. It is commonly used in image analysis for texture
segmentation, classification and. It is also of common use in multimedia
applications for compression purposes. Due to the diversity of image types, it
is impossible to have one universal model to cover all the possible types, even
when limiting ourselves to simple texture cases. Various models have been
proposed and each has its advantages and drawbacks. For textures, co-occurrence
matrices are a standard choice but they are in general difficult to compute and
are difficult to use for modeling. We will concentrate here on Markov Random
Field (MRF) which have been widely and very successfully used in the last 15
years.
Markov random fields belong to the
statistical models of images. Each pixel of an image can be viewed as a random
variable. An image c