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Markov Random Fields are a really popular way to look at an image, but I can't find a direct reference to them being implemented in OpenCV. Perhaps they are named differently, or are built from some indirect method.
As the title states, are MRFs implemented in OpenCV? And if not, what is the popular way to represent them?
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A Markov Random Field is a graph whose nodes model random variables, and whose edges model desired local influences among pairs of them. Local influences propagate globally, leveraging the connectivity of the graph. Here is an example. Consider an image on a rectangular grid. It's composed of pixels.
Markov random fields find application in a variety of fields, ranging from computer graphics to computer vision, machine learning or computational biology, and information retrieval. MRFs are used in image processing to generate textures as they can be used to generate flexible and stochastic image models.
A Conditional Random Field (CRF) is a form of MRF that defines a posterior for variables x given data z, as with the hidden MRF above. Unlike the hidden MRF, however, the factorization into the data distribution P (x|z) and the prior P (x) is not made explicit [288].
The MRF models the joint distribution of a blurred image and the corresponding unblurred image. From: Probabilistic Graphical Models for Computer Vision, 2020.
OpenCV deals mostly with statistical machine learning rather than things that go under the name Bayesian Networks, Markov Random Fields, or graphical models.
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