| 基于多尺度马尔可夫随机场的图像分割 |
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| 引用本文： | 汪西莉,焦李成.基于多尺度马尔可夫随机场的图像分割[J].计算机科学,2003,30(7):174-176. |
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| 作者姓名： | 汪西莉 焦李成 |
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| 作者单位： | 1. 西安电子科技大学雷达信号处理国家重点实验室,西安,710071;陕西师范大学计算机学院,西安,710062
2. 西安电子科技大学雷达信号处理国家重点实验室,西安,710071 |
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| 基金项目： | 国家自然科学基金(60133010)，教育部博士点基金 |
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| 摘 要： | 离散马尔可夫随机场(MRF)模型是贝叶斯图像分割中最常用的工具。一般采用双MRF,一个随机场对应于观测图像,另一个随机场对应于未知的分类标号,通过迭代的算法将图像的局部信息逐步传递到整个图像,以求得分割标号的最大后验概率(MAP)或最大后验边缘概率(MPM)估计。近年来提出的多尺度MRF模型(或称因果MRF、分层MRF模
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| 关 键 词： | 图像分割 图像像素 多尺度马尔可夫随机场 图像边缘 图像处理 |
Image Segmentation Based on Multiscale Markov Random Field |
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| WANG Xi-Li JIAO Li-Cheng.Image Segmentation Based on Multiscale Markov Random Field[J].Computer Science,2003,30(7):174-176. |
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| Authors: | WANG Xi-Li JIAO Li-Cheng |
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| Abstract: | The noniterative algorithm of multiscale MRF has much lower computing complexity and better result than its iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causality is consistent with the character of images. Maximizer of the posterior marginals (MPM)algorithm of multiscale MRF model is presented for only one image can be obtained in image segmentation. EM algorithm for parameter estimate is also given. Experiments demonstrate that comparing with iterative ones, the proposed algorithms have the characteristics of greatly reduced computing time and better segmentation results. This is more notable for large images. |
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| Keywords: | Multiscale markov random field(MRF) Noniterative algorithm Iterative algorithm Maximizer of the posterior marginals (MPM) Expectation maximization (EM) |
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