Classification of wood surface texture based on Gauss-MRF Model

Gauss-MRF 的基础理论被详细说明， 2-5 命令 Gauss-MRF 模型被建立。2-5 的参数为 300 件木头样品订 Gauss-MRF 模型“表面质地被使用 LMS 也估计。数据分析显示出那： 1 )不同质地参数有清楚的散布分布， 2 )质地的主要方向是 Gauss-MRF 参数的最大的参数代表的方向，并且 3 )为有一样的主要方向的那些样品，质地越好，相应参数是越多 greater ，并且更小另外的参数是；并且 Gauss-MRF 的顺序越高，质地越清楚地被描述。在第二个顺序 Gauss-MRF 模型的条件上，参数 B1，正切的质地的 B2 比光线的质地的小，当正切的质地的 B3 和 B4 比光线的质地的大时。根据分开的标准的价值，第五 orderGauss-MRF 的参数为 Hamming 神经网络分类被用作特征向量。作为结果，正确性的比率到达 88% 。

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 rexture 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%.