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Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique

机译：使用图像处理技术的白人参质量的非破坏性评估系统

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A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26 percent, which is comparable to the 30 percent in case of manual grading. The grading rate was one sample per a second.
机译：基于机器视觉技术和人工神经网络的实时白人人参质量评估系统是为了代替当前的手动分级，并测试其效率。该系统由输送机，图像采集系统与样品检测传感器同步，图像处理和决策系统。开发了Windows系统下运行的软件。该算法包括（a）图像采集和预处理的三个连续阶段，（b）使用人工神经网络的数学特征提取，（c）等级决定。诸如面积比，灰度级，灰度级直方图的偏差和运行段的数量的数学特征是从样本的五个等分割部分提取的。人工神经网络模型用于将样品分类为三种分级类别。该系统的分级误差约为26％，与手动分级的30％相当。分级率为每秒一个样品。

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