Triplet Markov random fields (TMF) model is suitable for dealing with multi-class segmentation of non-stationary, non-Gaussian SAR images.
三重马尔科夫随机场(TMF)模型非常适合处理非平稳、非高斯图像的分割问题。
This article proposed a new type of MRF (Markov random fields) hybrid multi-order prior for Bayesian reconstruction, which combines quadratic smoothness priors of different orders.
基于贝叶斯重建，本研究提出了一种应用于贝叶斯重建中新的综合了二次一阶先验和二次二阶先验的马尔可夫随机场混合多阶先验。
Due to the problems to infrared image segmentation using Markov random fields, a method for infrared image segmentation based on triplet Markov fields using mixture gauss model was proposed.
针对马尔可夫随机场在红外图像分割方面存在的问题，给出了一种基于混合高斯模型的三马尔可夫场红外图像分割算法。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的最大后验概率估计。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的最大后验概率估计。
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