摘要
用改进的变形雅可比(p=4,q=2)-傅立叶矩对玫瑰花等6种花类草药的显微结构图像进行特征提取,并用最小平均距离规则,对该6种花类草药28个显微特征点的368种变形图像进行了训练和仿真识别实验,平均识别率达98.1%。因此,该研究结果可为蒙药材显微识别和鉴定的数字化提供重要的技术手段。
Miccrosoopic characteristics of several Mongolian Herbal flowers were extracted by improved Pseudo-Jacobi (p=4, q=2)-Fourier Moments (PJFM's), and 368 different versions of 28 microscopic characteristics of these herbs were identified by using the minimum-mean-dlstance rule. The experimental results showed that the average identification rate reaches as high as 98.1%. Therefore, this study can provide new techniques for digitalization and visualization of microsoopic characteristics of Mongolian Herbs.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2008年第1期146-149,共4页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(60467001)
内蒙古自然科学基金资助项目(200408020109)
内蒙古自治区高等学校科学研究资助项目(NJ03037)
关键词
蒙草药
显微特征点
变形雅可比-傅立叶矩
图像识别
Mongolian Herbs Microscopic characteristics Pseudo-Jacobi-Fourier Moments(PJFM's) Image identification