摘要
提出了利用边缘检测提取物体特征的算法,首先利用动态边缘检测算法提取物体的边缘,接着计算边沿到重心的距离,再将特征长度归一化。利用获取的特征训练SVM分类器。最后利用粮食图像对该方法进行了仿真实验。实验表明,提出的方法能有效地提取边缘特征,并且具有较高的分类正确率。
This paper proposed an object detection approach using edge detection. Firstly,it uses dynamic edge detection algorithm to extract the grain edge, secondly, computes the distance between the edge points and the center of gravity, and obtains a vector as the primitive feature which will be processed via amplitude and length normalization and become the feature. The features are used to train the SVM classifier. At last, we conducted simulation experiments using grain image, and the experiment results reveal that the proposed method can efficiently extract the edge feature, and has higher classification accurate rate.
出处
《计算机科学》
CSCD
北大核心
2013年第7期280-282,共3页
Computer Science
基金
安徽省教育厅高校科学研究项目(KJ2012A065)资助
关键词
边缘检测
边界描述
特征提取
SVM分类器
Edge detection, Edge representation,Feature extraction, SVM classifier
