研究了茶叶图像处理的关键技术和方法,包括茶叶图像的阈值变换、中值滤波、图像标记、边界轮廓跟踪等,解决了茶叶外形特征参数的快速准确提取和计算;设计了基于数字图像处理的茶叶外形特征检测软件,实现了茶叶图像的实时采集、图像预处理、特征参数提取等功能;最后运用该软件提取3种不同茶叶样品共108片茶叶图像的17项外形特征参数,并以其中6项参数作为BP神经网络的输入,建立不同茶叶样品与外形特征之间的BP神经网络预测模型,并对30片未知茶叶样本进行预测,鉴别准确率达到80％。
Abstract
The key methods of tea image processing were studied, including threshold transforming, median filter, image mark and boundary following. These methods solved the problem of how to accurately extract and calculate the characteristic parameters of tea shape. The software for tea quality detection based on digital image was developed. The functions, such as collection of tea image, image processing and extraction of characteristic parameter, could be accomplished. Using the software, 17 shape characteristic parameters were collected from 108 pieces of three different kinds of tea. And 6 characteristic parameters were used to build the back propagation-artificial neural network (BP-ANN) model. The variety from thirty unknown samples were predicted by this model and the recognition rate of eighty percent was achieved.
关键词
BP神经网络 /
茶叶 /
特征参数 /
图像处理
Key words
BP neural network /
characteristic parameter /
image processing /
tea
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参考文献
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基金
国家自然科学基金（30600371）、教育部重点项目（109090）、浙江省自然科学基金（Y3080277）
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