摘要
通过对黄瓜病害图像的准确分析,有效提取了图像的底层特征,建立了8种常见黄瓜病害的高斯混合模型(Gaussian Mixture Model,GMM),并利用最大期望算法(Expectation-Maximization,EM)估计GMM的参数,精确描述了8种黄瓜病害的特征分布,从而提高了对黄瓜病害的正确识别和为害情况的准确把握,为实现黄瓜病害的实时与准确的预测和防治提供了理论依据。
Based on the accurate analysis of cucumber disease images,the low-level feature of images was effectively extracted,and Gaussian Mixture Model（GMM） for 8 common cucumber diseases was built.The parameters of GMM were estimated by the algorithm of Expectation Maximum（EM） to accurately characterize the feature distribution of 8 cucumber diseases,thus increased the correct identification of cucumber diseases and accurate grasping of damage conditions,and provided basis for achievement of real-time and accurate prediction of cucumber diseases.
出处
《安徽农业科学》
CAS
北大核心
2011年第34期21096-21099,共4页
Journal of Anhui Agricultural Sciences
基金
国家自然科学基金项目(60903066
0985244)
北京市自然科学基金项目(4102049)
教育部新教师基金项目(2009-0009120006)
中央高校基本科研业务费项目(2010-0008030)
关键词
黄瓜病害
图像处理
数学建模
高斯混合模型
Cucumber disease
Image processing
Mathematical modeling
Gaussian Mixture Model
