殷勇,陶凯,于慧春.基于机器视觉的苹果分级中特征参量选择方法[J].农业机械学报,2012,43(6):118-121,127.
Yin Yong,Tao Kai,Yu Huichun.Feature Selection Method for Apple Grading Based on Machine Vision[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(6):118-121,127.
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基于机器视觉的苹果分级中特征参量选择方法   [下载全文]
Feature Selection Method for Apple Grading Based on Machine Vision   [Download Pdf][in English]
  
DOI:10.6041/j.issn.1000-1298.2012.06.022
中文关键词:  苹果  分级  图像处理  特征选择  主成分分析  统计量
基金项目:河南省科技创新杰出青年资助项目(624420017)
作者单位
殷勇 河南科技大学 
陶凯 河南科技大学 
于慧春 河南科技大学 
中文摘要: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Wilks Λ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%。
Yin Yong  Tao Kai  Yu Huichun
Henan University of Science and Technology;Henan University of Science and Technology;Henan University of Science and Technology
Key Words:Apple, Grade, Image, Feature selection, Principal component analysis, Statistic
Abstract: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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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