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基于高斯-马尔可夫随机场的木材表面纹理分类
引用本文:王克奇,白雪冰.基于高斯-马尔可夫随机场的木材表面纹理分类[J].林业研究,2006,17(1):57-61.
作者姓名:王克奇  白雪冰
作者单位:东北林业大学,哈尔滨,150040;东北林业大学,哈尔滨,150040
基金项目:Municipal Natural Science Foundation of Harbin,黑龙江省自然科学基金
摘    要:阐述了高斯-马尔可夫随机场模型的基本原理,建立了木材表面纹理的2-5阶高斯-马尔可夫随机场(Gauss-MRF)模型,用最小二乘法估计了300个木材样本表面纹理的2-5阶Gauss-MRF参数。数据分析表明,各不同纹理特征参数之间具有明显的分布性;Gauss-MRF参数值最大的参数所表示的纹理集聚方向为纹理的主方向;对于纹理主方向相同的样本,纹理越细致,其相应参数越大,而其他参数越小;Gauss-MRF阶数越高,纹理描述越细致;在2阶Gauss-MRF模型情况下,弦切纹理的B1参数大于径切纹理的B1;弦切纹理的B2、B3、B4分别小于径切纹理的B2、B3、B4。根据分离判据的值,确定以5阶Gauss-MRF参数为特征向量进行初步聚类,总体正确率为88%。

关 键 词:木材表面纹理  高斯-马尔可夫随机场  特征参数  参数估计  分离判据  聚类
文章编号:1007-662X(2006)01-0057-05
收稿时间:2005-09-01
修稿时间:2005-10-08

Classification of wood surface texture based on Gauss-MRF model
Ke-qi Wang,Xue-bing Bai.Classification of wood surface texture based on Gauss-MRF model[J].Journal of Forestry Research,2006,17(1):57-61.
Authors:Ke-qi Wang  Xue-bing Bai
Institution:(1) Northeast Forestry University, Harbin, 150040, P. R. China
Abstract:The basal theory of Gauss-MRF is expounded and 2–5 order Gauss-MRF models are established. Parameters of the 2–5 order Gauss-MRF models for 300 wood samples’ surface texture are also estimated by using LMS. The data analysis shows that: 1) different texture parameters have a clear scattered distribution, 2) the main direction of texture is the direction represented by the maximum parameter of Gauss-MRF parameters, and 3) for those samples having the same main direction, the finer the texture is, the greater the corresponding parameter is, and the smaller the other parameters are; and the higher the order of Gauss-MRF is, the more clearly the texture is described. On the condition of the second order Gauss-MRF model, parameter B1, B2 of tangential texture are smaller than that of radial texture, while B3 and B4 of tangential texture are greater than that of radial texture. According to the value of separated criterion, the parameter of the fifth order Gauss-MRF is used as feature vector for Hamming neural network classification. As a result, the ratio of correctness reaches 88%. Biography: WANG Ke-qi (1958–), Professor in Northeast Forestry University, Harbin 150040, P. R. China
Keywords:Wood surface texture  Gauss-MRF  Feature parameter  Parameter estimation  Separation judgment  Classification  
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