Markov Random Field in Visual Information Processing
视觉信息处理中的马尔可夫随机场

Probabilistic graphical models (PGM) is widely applied in visual information processing for the intrinsic uncertainty in visual information, and followed by a group of researchers recently. PGM offers a number of advantages for resolving variety problems in visual information processing, in which Markov Random Field (MRF) can be used to model pixel level information processing based on the development of high efficiency inference algorithms. In this paper, we shortly introduced concepts of PGM, and gave detailed analysis and discussion on the definition, features and inference of MRF followed by typical examples of its application in computer vision.