CAJ | 학술논문

为了提高对木材纹理识别的精度,提出了一种基于融合灰度共生矩阵与高斯-马尔可夫随机场纹理参数的特征级数据融合木材纹理模式识别方法.首先,分别获取了以上两种木材纹理特征参数;然后,使用模拟退火算法将两种不同类型的纹理特征量在特征层上进行了融合.利用融合后的特征对木材纹理样本进行识别,BP神经网络分类器的识别率达到97.00%,表明数据融合后的特征参数对木材纹理识别是十分有效的.
위료제고대목재문리식별적정도,제출료일충기우융합회도공생구진여고사-마이가부수궤장문리삼수적특정급수거융합목재문리모식식별방법.수선,분별획취료이상량충목재문리특정삼수;연후,사용모의퇴화산법장량충불동류형적문리특정량재특정층상진행료융합.이용융합후적특정대목재문리양본진행식별,BP신경망락분류기적식별솔체도97.00%,표명수거융합후적특정삼수대목재문리식별시십분유효적.
In order to enhance the precision of wood texture recognition,a kind of wood surface texture recognition method is proposed,which uses Gray Level Co-occurence Matrix(GLCM) and Gaussian-Markov Random Field(GMRF) on feature level data fusion.First,two sorts of feature parameters above are extracted respectively.Next,two sorts of texture features are fused on the fea-ture level by simulated annealing algorithm.With the fused features,the recognition rate of the integrated BP neural network to the wood textural samples reaches to 97.00%.The result indicates that to recognize wood with the fused features is quite effective.