文件名称:image-segmentation
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针对目前传统的枸杞分级主要采用人工方法, 费时费力且效率不高的缺点, 提出了一种基于机器视觉技术对枸杞
进行自动分类的方法。 采用数字图像处理技术对枸杞图像进行了预处理、 分割 , 从而提取枸杞的色泽、 大小及形状等特征
参数; 用 K-means 算法对特征进行聚类, 得到枸杞相应等级的基准; 根据聚类分析得到的基准采用最小距离分类器对枸杞
进行分级。 实验结果表明 , 该方法能够准确快速地对不同色泽和大小的枸杞进行分类。-Traditional wolfberry sorting primarily uses artificial method. It has time-consuming and inefficient shortcomings. An
automatic wolfberry classification method based on machine vision is proposed. This paper uses digital image processing technology for wolfberry image pre-processing, segmentation and extraction of characteristic parameters of color, size and shape it
uses the K-means clustering feature to get the baseline of wolfberry appropriate level it grades wolfberry by minimum distance
classifier based on the trained benchmark. The experimental results show that this method can classify different colors and sizes
of wolfberry more accurately and quickly.
进行自动分类的方法。 采用数字图像处理技术对枸杞图像进行了预处理、 分割 , 从而提取枸杞的色泽、 大小及形状等特征
参数; 用 K-means 算法对特征进行聚类, 得到枸杞相应等级的基准; 根据聚类分析得到的基准采用最小距离分类器对枸杞
进行分级。 实验结果表明 , 该方法能够准确快速地对不同色泽和大小的枸杞进行分类。-Traditional wolfberry sorting primarily uses artificial method. It has time-consuming and inefficient shortcomings. An
automatic wolfberry classification method based on machine vision is proposed. This paper uses digital image processing technology for wolfberry image pre-processing, segmentation and extraction of characteristic parameters of color, size and shape it
uses the K-means clustering feature to get the baseline of wolfberry appropriate level it grades wolfberry by minimum distance
classifier based on the trained benchmark. The experimental results show that this method can classify different colors and sizes
of wolfberry more accurately and quickly.
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基于机器视觉的枸杞分级方法.pdf