Abstract:An adaptive neighborhood approach is proposed. The Markov random fields (MRF) segmentation approach with adaptive neighborhood systems is utilized to preserve detail features and border areas and to improve the segmentation effect. Bayesian inference is applied to integrate the different information sources of local image around the pixels. To improve the reliability of the belief value and the adaptivity, fuzzy c-means (FCM) clustering is introduced in Bayesian network. Thus, the selection of the neighborhood in the region label process need not depend on the known priori knowledge by applying the FCM. The neighborhood with the highest belief value in the threshold scope is chosen to compute the MRF region label process. Experimental results demonstrate that the segmentation effect of the proposed algorithm is superior to that of the classical MRF and hidden Markov random field with detail structures well preserved.
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