| 图象分析中的松驰标记法 |
| |
| 引用本文： | 戴剑彬,张大力.图象分析中的松驰标记法[J].中国图象图形学报,1998,3(2):96-99. |
| |
| 作者姓名： | 戴剑彬 张大力 |
| |
| 作者单位： | 清华大学自动化系 |
| |
| 摘 要： | 松驰标记法是指对图中的每个目标进行标记指派，利用先验上下文信息进行迭代，寻求最大协调标记集的一种方法。此文推导了一种新的概率松驰法，分析了随机松驰法的迭代公式，利用马尔科夫随机场（ＭＲＦ）与吉布斯（Ｇｉｂｂｓ）分布的等价性来计算局部特性概率，用最大熵（ＭＥ）原理对条件邻域概率进行估计。最后对概率松驰法和随机松驰法进行了比较。
|
| 关 键 词： | 概率松驰，随机松驰，马尔科夫随机场，吉布斯分布，最大熵 |
| 修稿时间： | 1991年4月2日 |
Relaxation Labeling in Image Analysis |
| |
| Dai Jianbin and Zhang Dali.Relaxation Labeling in Image Analysis[J].Journal of Image and Graphics,1998,3(2):96-99. |
| |
| Authors: | Dai Jianbin and Zhang Dali |
| |
| Abstract: | Relaxation labeling refers to a class of algorithms for assigning a label to each object in a
graph, by iterating a transformation until a fixed point is reached. A probabilistic relaxation method is analytically derived in this paper, and a stochastic relaxation algorithm is also carried out step by step. We employ the MRF-Gibbs equivalence to calculate the local characteristics of the MRF, and take the maximum entropy (ME) estimate as the conditional neighborhood probabilities. At the last section of the paper, the two distinct approaches are compared and contrasted. |
| |
| Keywords: | Probability relaxation Stochastic relaxation Markov random fields Gibbs distribution Maximum entropy |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《中国图象图形学报》浏览原始摘要信息 |
|
点击此处可从《中国图象图形学报》下载免费的PDF全文 |
