基于小波描述子的水果果形分类

水果果形是水果分级的重要指标之一,该文提出了一种基于小波描述子的水果果形分类方法.通过提取水果轮廓计算出半径序列,并进行归一化处理,对归一化后的半径序列进行小波变换提取小波描述子;分别截取小波描述子12、20、36和67个系数点对水果边界进行重建.结果表明：用36个系数点就可较好地重建果形,匹配率为98.64%,用67个系数点可达相当高的精度,为99.96%;选取36个系数点作为果形特征,并运用核主成分分析（KPCA）提取分类所需的7个主要特征输入径向基函数（radial basis function,RBF）神经网络进行分类,发现该方法分级准确率可达90%,效果优于傅里叶描述子,是一种有效地...

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Published in	Zhejiang daxue xue bao. Journal of Zhejiang University. Agriculture and life sciences. Nong ye yu sheng ming ke xue ban Vol. 36; no. 3; pp. 322 - 328
Main Author	刘仰龙王从庆高珏许荣华
Format	Journal Article
Language	Chinese
Published	南京航空航天大学,自动化学院,江苏,南京,210016%扬州福尔喜果蔬汁机械有限公司,江苏,扬州,225105 2010
Subjects	小波变换小波描述子果形分析水果分级小波变换小波描述子果形分析水果分级
Online Access	Get full text
ISSN	1008-9209
DOI	10.3785/j.issn.1008-9209.2010.03.014

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Abstract	水果果形是水果分级的重要指标之一,该文提出了一种基于小波描述子的水果果形分类方法.通过提取水果轮廓计算出半径序列,并进行归一化处理,对归一化后的半径序列进行小波变换提取小波描述子;分别截取小波描述子12、20、36和67个系数点对水果边界进行重建.结果表明：用36个系数点就可较好地重建果形,匹配率为98.64%,用67个系数点可达相当高的精度,为99.96%;选取36个系数点作为果形特征,并运用核主成分分析（KPCA）提取分类所需的7个主要特征输入径向基函数（radial basis function,RBF）神经网络进行分类,发现该方法分级准确率可达90%,效果优于傅里叶描述子,是一种有效地描述水果果形的方法.
AbstractList	水果果形是水果分级的重要指标之一,该文提出了一种基于小波描述子的水果果形分类方法.通过提取水果轮廓计算出半径序列,并进行归一化处理,对归一化后的半径序列进行小波变换提取小波描述子;分别截取小波描述子12、20、36和67个系数点对水果边界进行重建.结果表明：用36个系数点就可较好地重建果形,匹配率为98.64%,用67个系数点可达相当高的精度,为99.96%;选取36个系数点作为果形特征,并运用核主成分分析（KPCA）提取分类所需的7个主要特征输入径向基函数（radial basis function,RBF）神经网络进行分类,发现该方法分级准确率可达90%,效果优于傅里叶描述子,是一种有效地描述水果果形的方法. TP391.4%S661.1; 水果果形是水果分级的重要指标之一,该文提出了一种基于小波描述子的水果果形分类方法.通过提取水果轮廓计算出半径序列,并进行归一化处理,对归一化后的半径序列进行小波变换提取小波描述子;分别截取小波描述子12、20、36和67个系数点对水果边界进行重建.结果表明:用36个系数点就可较好地重建果形,匹配率为98.64%,用67个系数点可达相当高的精度,为99.96%;选取36个系数点作为果形特征,并运用核主成分分析(KPCA)提取分类所需的7个主要特征输入径向基函数(radial basis function,RBF)神经网络进行分类,发现该方法分级准确率可达90%,效果优于傅里叶描述子,是一种有效地描述水果果形的方法.
Author	刘仰龙王从庆高珏许荣华
AuthorAffiliation	南京航空航天大学自动化学院,江苏南京210016 扬州福尔喜果蔬汁机械有限公司,江苏扬州225105
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Author_FL	XU Rong-hua GAO Jue LIU Yang-long WANG Cong-qing
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