摘要
介绍了采用三帧差分法实现谷物害虫图像恢复与提取的方法,利用图像的一阶灰度值直方图和图像的目标区域,自动提取静态储粮害虫图像的纹理等特征。针对于相对特征维数而言样本数很少的特点,提出利用多类SVM分类器的方法实现对储粮害虫的快速鉴定和分类。实验结果表明,相比传统的神经网络,SVM在有限样本情况下具有良好的泛化能力。
A method of using three image differences to restore and extract the pest images is introduced. With reference to the fist order gray histogram of pest images and their graphic target zones, a technique is provided to extract the texture eigenvalue. A multi-class support vector machine is proposed to implement the quick identification and classification of grain pests according to small amount samples compared to the dimensions. The test shows the better generalization ability of SVM than that of ANN under the conditions of limited training samples,
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第9期167-169,共3页
Computer Engineering
基金
国家"粮食丰产科技工程"基金资助项目(2004BA520A)
