A novel method of softwood species computer automatic recognition through cross-sectional microscopic images is proposed in this paper. The method extracts PCA (principle component analysis) feature of wood images, generate "EigenTrees" , and then use SVM(support vector machine) to classify samples in feature space. Eight kinds of softwoods species, twelve samples in each species are used in our experiment. Using leave-one-out cross-validation (LOOCV) , wood recognition experiments are carried out under different conditions on image split methods, classification algorithms of nearest neighbor and SVM, and various norm distances. The results of these experiments show that wood recognition by parts of wood micro-texture is possible under certain conditions.%提出通过横切面显微图像对针叶材树种进行计算机识别的方法.该方法通过提取图像的PCA特征,生成“特征树”,然后采用SVM对样本进行分类.使用8种针叶材,每种12个样本,并采用留一交叉验证,对图像的分割方法、最近邻与SVM分类算法和不同范数距离下的识别效果进行试验.结果表明通过部分木材微观的纹理结构进行木材识别的可能性.
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