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| 一种分层马尔可夫图像模型及其推导算法 |
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汪西莉1,2, 刘芳3, 焦李成3
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1.西安电子科技大学,雷达信号处理国家重点实验室,陕西,西安,710071;2.陕西师范大学,计算机学院,陕西,西安,710062;3.西安电子科技大学,计算机学院,陕西,西安,710071
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| 摘要: |
| 离散分层马尔可夫随机场(MRF)模型由于层间具有了因果性,因而其非迭代的推导算法比非因果的马尔可夫随机场模型的迭代算法复杂度低得多,结果更精确.针对图像分割问题中观测数据有限的情况,提出了一种新的基于离散分层MRF的半树模型,推导出了它的最大后验边缘概率(MPM)算法.半树模型不仅继承了一般分层模型快速、误分类少的优点,还避免了计算中遇到的数值下溢问题,减轻了分层模型带来的块现象,尤其适合大幅面图像的处理. |
| 关键词: 离散分层马尔可夫随机场 半树模型 非迭代算法 迭代算法 最大后验边缘概率 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60133010, 60073053 (国家自然科学基金); the National Research Foundation for the Doctoral Program of Higher Education of China (国家教育部博士点基金) |
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| A Hierarchical Markov Image Model and Its Inference Algorithm |
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WANG Xi-Li,LIU Fang,JIAO Li-Cheng
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| Abstract: |
| The noniterative algorithm of discrete hierarchical MRF (Markov random field) model has much lower computing complexity and better result than its iterative counterpart of noncausal MRF model, since it has causality property between layers. A new model based on the hierarchical MRFhalf tree model is proposed for only one image can be obtained in image segmentation, whose MPM (maximizer of the posterior marginals) algorithm is inferred too. The proposed model not only inherits the advantages of general hierarchical MRF model but also does better: it makes large image more tractable within much less time, prevents data underflow appeared in computing, and alleviates the block artifacts occurred in hierarchical models. It is especially fit for large scale images. |
| Key words: discrete hierarchical Markov random field half tree model noniterative algorithm iterative algorithm maximizer of the posterior marginals (MPM) |
