基于机器视觉的苹果分级中特征参量选择方法

为提高基于数字图像的苹果分级的准确性,常提取多特征信息。然而,使用多特征信息分级时会存在信息冗余等问题。为此,运用主成分分析(PCA)来融合特征参量,并借助WilksΛ统计量选择对分级有显著作用的主成分;然后依据各特征参量对所选择主成分的贡献率筛选特征参量。Fisher判别分析(FDA)结果表明:使用所选择的特征参量进行苹果分级,分级效果明显优于特征选择前,分级正确率和交叉验证正确率分别提高了2.0%和1.5%。

Feature Selection Method for Apple Grading Based on Machine Vision

In order to improve the accuracy of apple grading in digital image processing system,the multi-feature information was extracted for describing the apple features.However,this method may result in information redundancy and so on.So,the principal component analysis(PCA) was used to carry out information fusion of the feature parameters,and with the aid of Wilks Λ statistic the principal components(PC) which could promote grading results were selected.Then some features used in grading were selected based on the contribution rate to selected PC.The results of Fisher discriminate analysis(FDA) showed that the grading effect corresponding to the selected features was better than that of all features,and the grading accuracy and the cross-validation accuracy rose by 2.0% and 1.5%,respectively.