常州大学学报（自然科学版）2017，Vol.29Issue(3)：60-68,9.DOI:10.3969/j.issn.2095-0411.2017.03.009
基于核PCA与SVM算法的木材缺陷识别
Application of KPCA and SVM to Wood Defect Recognition
马旭 1刘应安 1业宁 1闫贺1
作者信息
- 1. 南京林业大学 信息科学技术学院,江苏 南京 210037
- 折叠
摘要
Abstract
Wood defect is an important factor affecting the wood industrialization promotion.A reasonable wood defect recognition method can effectively avoid the waste of resources caused by wood defects in the practical application.At the same time it can raise the actual utilization of wood.Considering the nonlinear characteristic of wood defects, a new wood defect recognition method is proposed.Firstly, mapping wood original nonlinear data from low dimensional to high dimensional linear feature space using the polynomial kernel function.And then the mapping space of linear dimension reduction processing samples.The purpose is to extract the feature parameters to the samples.Next by means of the SVM model, the polynomial kernel function is selected to complete the wood defect identification.The experimental results show that the proposed method has higher recognition accuracy and efficiency by comparing the data from experiment and the measured data.关键词
木材缺陷/核函数/主成分提取/支持向量机Key words
wood defect/kernel function/PCA/SVM分类
轻工纺织引用本文复制引用
马旭,刘应安,业宁,闫贺..基于核PCA与SVM算法的木材缺陷识别[J].常州大学学报（自然科学版）,2017,29(3):60-68,9.
