Markov Random Field

MRF has become a significant statistical signal modeling technique in image processing and computer vision. Generally speaking, the MRF model assumes that the information contained in a particular location is affected by its neighboring local structure of a given image rather than the whole image. In other words, the estimation of pixel's properties, such as intensity, texture, color, etc., closely relates to a neighborhood of pixels, and this dependency can be characterized by means of a local conditional probability distribution. This hypothesis can

| Non-edge pixel

|_| Edge pixel i|i|i Neighboring region

Figure 8.1: Illustration of MRF neighborhood and edge constraint. s and g are no-edge pixels belonging to different regions and h is an edge pixel within the s neighborhood.

reduce the complexity of the image modeling and provides a convenient and consistent way of describing the observed images.




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