Pairwise and Hidden Markov Random Fields in Image Segmentation

Abstract

The purpose of this paper is to identify the similarities and differences between two image restoration approaches based on Markov field modeling. The first one is the wellknown Bayesian approach which models the unknowns with a Markovian prior. In the second approach, as proposed by Pieczynski and Tebbache [1], the pair unknowns-observations as a whole is considered Markovian. The two approaches are compared based on their posterior distribution, synthetic results and real examples, when applied to the segmentation of degraded images.

Robust Segmentation Based on Salient Region Detection Coupled Gaussian Mixture Model

“…In 2016, Liqiu Chen [20] published a paper proposing a method for dynamic texture segmentation of inter-scale background based on Markov random fields. In 2021, hidden Markov random fields [21] have been proposed. The spatial information relation of pixels is constructed by it, and a new context constraint is formed to carry out image segmentation.…”

“…Common clustering segmentation includes K-means segmentation and fuzzy set c-means segmentation. Statistical methods include the finite mixture model and Markov model [17][18][19][20][21][22][23]. There are three kinds of segmentation methods based on deep learning: semantic segmentation [24], instance segmentation [25] and panoramic segmentation [26].…”