【摘要】 分层马尔可夫随机场 (MRF)图象模型由于层间具有因果关系 ,且这种因果关系符合图象的性质 ,使基于该模型的图象处理时间比平面MRF模型所用的时间大为减少 .针对作者提出的一种新的分层马尔可夫图象模型———不完全分层模型 ,导出EM算法以估计模型参数 .算法继承了分层模型非迭代算法运算速度快的优点 ,并因为模型结构的简化进一步减少了计算量 ,算法在模型的最上层加入了平面节点间信息的交互 ,以较少的计算换来了更加精确的参数估计结果 .算法用于图象非监督分割的实验表明 ,和分层模型算法相比 ,其处理速度更快、由所估计的参数得到了更好或相当的分割结果 ,尤其适合大幅面图象的处理更多还原

【Abstract】 Hierarchical MRF image model has causality property between layers,and the causality is consistent with the characteristics of images.So the processing time of such models is much less than that of the plain MRF models.An expectation maximization(EM) algorithm estimating parameters of incomplete hierarchical MRF model,which is a new hierarchical MRF image model we presented,is deduced.The advantage of less time cost possessed by the non iterative algorithm of hierarchical models is inherited.Time is further reduced due to simplified model structure.The interaction between neighbor nodes on the top layer is considered,which results in more accurate estimate values with less computing cost.The algorithm is used in unsupervised image segmentation.The experimental results demonstrate that it is characterized by high speed and better results compared with that of hierarchical models.It is more fit for large images.更多还原