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| 引用本文: | 常敏,杜嘉,马晓涛,沈凯,李柏萱,秦晓飞.基于形状和饱满度特征精选枸杞的方法[J].包装工程,2018,39(11):182-187. |
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| 基于形状和饱满度特征精选枸杞的方法 |
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常敏1, 杜嘉1, 马晓涛2, 沈凯1, 李柏萱1, 秦晓飞1
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1.上海理工大学 光电信息与计算机工程学院，上海 200093;2.河北软件职业技术学院，河北 071000
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| 摘要: |
| 目的 为了提高枸杞分选效率且使分选结果便于统一包装，提出一种在机器视觉技术下多特征组合的枸杞精选方法。方法 枸杞的形状和饱满度是精选的2个重要特征和依据，首先通过数字图像处理技术获取枸杞图像的横径纵径数据比值，并将其作为形状特征参数；其次获取枸杞质量和枸杞图像面积的比值，将其作为饱满度特征参数。基于这2类特征参数进行聚类分析，建立BP神经网络的枸杞多特征精选分类器。结果 枸杞的形状类型不同，其形状特征和饱满度特征值也不同，通过BP神经网络分类器显示不同形状类型的枸杞聚类。该方法可以有效地对枸杞形状类型进行精选，精选准确率达到97.6%。结论 根据形状和饱满度特征组合的分选效果最佳，该方法可以实现枸杞形状类型的精选。 |
| 关键词: 特征参数 神经网络 枸杞 形状 饱满度 |
| DOI：10.19554/j.cnki.1001-3563.2018.11.032 |
| 分类号:TP391.41 |
| 基金项目:国家重大科学仪器设备开发专项（2012YQ150087） |
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| The Method of Selecting Chinese Wolfberry Based on Shape and Plumpness Characteristics |
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CHANG Min1, DU Jia1, MA Xiao-tao2, SHEN Kai1, LI Bai-xuan1, QIN Xiao-fei1
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1.College of Photoelectric Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 20093, China;2.Hebei Software Institute, Heibei 071000, China
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| Abstract: |
| The work aims to propose a selection method of Chinese wolfberry in the multi-feature combination of machine vision technology, in order to improve the separation efficiency of Chinese wolfberry and enable the sorting result to be convenient for uniform packaging. The shape and plumpness of Chinese wolfberry were two important features and basis of selection. Firstly, through digital image processing technology, the transverse diameter and longitudinal diameter of the wolfberry image were obtained and regarded as the shape characteristic parameters; then, the ratio of the quality of Chinese wolfberry to its image area was obtained and regarded as its plumpness characteristic parameter. Based on the clustering analysis of these two kinds of characteristic parameters, the multi-feature selection classifier of Chinese wolfberry was established by BP neural network. When the shape type of Chinese wolfberry was different, its shape characteristic and plumpness characteristic value were different. The BP neural network classifier displayed the Chinese wolfberry cluster of different shape types. Such method could effectively select the shape type of Chinese wolfberry and the selection accuracy was up to 97.6%. The selection effect is the best when the shape and plumpness characteristics are combined. Such method can realize the selection of the shape type of Chinese wolfberry. |
| Key words: characteristic parameter neural network Chinese wolfberry shape plumpness |
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