CAJ | 학술논문

为实现外观相似的不同玉米品种的有效识别,提出了K-L变换与最小二乘支持向量机相结合的籽粒品种鉴别方法.采用标记算法及多尺度小波分析方法获得玉米单籽粒图像,应用K-L变换技术提取籽粒图像的特征数据,设计了二叉树型多类LS-SVM分类器,实现了对特征数据的有效分类.通过试验分析,确定了较为合理的状态空间维数(L=3),正确识别率可达95.3%.
위실현외관상사적불동옥미품충적유효식별,제출료K-L변환여최소이승지지향량궤상결합적자립품충감별방법.채용표기산법급다척도소파분석방법획득옥미단자립도상,응용K-L변환기술제취자립도상적특정수거,설계료이차수형다류LS-SVM분류기,실현료대특정수거적유효분류.통과시험분석,학정료교위합리적상태공간유수(L=3),정학식별솔가체95.3%.
A method for maize cultivars identification was proposed, by using the combination algorithm of K-L transform and least square support vector machines (LS-SVM).Firstly, labeling algorithm and multi-scale wavelet analysis were used to acquire single corn kernel images; secondly, K-L transform was employed to extract the features of corn kernel images; at last, the method of LS-SVM with a binary tree architecture was developed to identify maize cultivars by using feature data.According to the result of test, the reasonable state space dimension (L=3) is acquired, and this method gives a correct recognition rate of 95.3%.