逆向云在垩白识别中的应用  

Application of Backward Cloud to Chalkiness Detection

作　　者：石礼娟[1,2] 文友先[2] 牟同敏[3] 陈芳[1] 

机构地区：[1]华中农业大学理学院,武汉430070 [2]华中农业大学工程技术学院,武汉430070 [3]华中农业大学作物遗传改良国家重点实验室,武汉430070

出　　处：《农业机械学报》2009年第12期196-199,232,共5页Transactions of the Chinese Society for Agricultural Machinery

基　　金：湖北省重点科技攻关项目(20002P0603)

摘　　要：在云理论的基础上提出了一种无需人工干预的垩白识别方法。在此方法中,把垩白与非垩白定义为两个定性概念,以一个不对称云和一个对称云来表达垩白与非垩白,以两组数字特征分别描述垩白云与非垩白云。首先,利用动态阈值算法获得训练样本,然后设计逆向云发生器实现定量到定性的转换,最后根据两个云的隶属度函数,用极大值判定法来实现垩白区与非垩白区的分离。试验结果表明,云分类法分类精度高于传统的硬分类法。A method based on the cloud theory was developed to improve the automatic degree ano accuracy of chalkiness detection. In this method, without man＇s intervention, chalkiness and non-chalkiness were defined as two qualitative concepts. An asymmetrical cloud was used to represent chalkiness, and a symmetrical cloud was used to represent non-chatkiness. These two clouds were respectively described by two groups of digital characters. Firstly, dynamie threshold program was designed to acquire training samples for the two clouds. Secondly, backward cloud generators were developed to implement the transformation from the quantities to the qualitatives. Finally, maximum value judgment method was used to separate the chalky region from non-chalky region according to the membership function of each cloud. The result shows that the classification accuracy of cloud classifier is higher than the classification accuracy of traditional hard classifiers.

关 键 词：稻米 逆向云 垩白 识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]