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Position: Home > Articles > Method of clustering segmentation for tea sprouts based on sharpness function Hubei Agricultural Sciences 2020 (8) 154-157

基于清晰度的茶叶嫩芽聚类分割方法

作  者：
黄涛;方梦瑞;夏华鵾;左亮亮;吕军
单  位：
关键词：
茶叶嫩芽;清晰度;聚类;图像分割
摘  要：
针对自然环境下茶叶嫩芽图像分割易出现过分割和欠分割等问题,提出一种基于清晰度评价和颜色聚类级联的嫩芽图像分割方法,并结合Tenengrad梯度评价和滑动分割获取清晰度较高的图像区域,然后在RGB、HSV、Lab、YCbCr颜色模型下进行聚类分割。结果表明,选取Tenengrad梯度值的上四分位数作为清晰度初选阈值,漏选率为25%;在HSV颜色模型下,利用K-means聚类方法完成嫩芽图像分割,晴天和阴天环境下嫩芽图像分割精度分别为72.48%和77.83%,较直接K-means分割方法相比,假阳性率分别减少5.19%和2.03%。该方法能够实现自然环境下茶叶嫩芽图像的有效分割,减少欠分割和过分割,为茶叶智能采摘提供理论参考。
译  名：
Method of clustering segmentation for tea sprouts based on sharpness function
作  者：
HUANG Tao;FANG Meng-rui;XIA Hua-kun;ZUO Liang-liang;LYU Jun;School of Information Engineering,Huangshan University;
关键词：
tea sprouts;;sharpness function;;cluster;;image segmentation
摘  要：
Aiming at the problem of over-segmentation and under-segmentation in tea sprouts segmentation under natural environment,a new method based on sharpness function evaluation and color clustering was proposed. The image regions with high sharpness function were obtained by combining Tenengrad gradient evaluation and slider analysis,then K-means clustering segmentation under RGB,HSV,Lab,YCbCr color models were finished. Experiments showed that the upper quartile of Tenengrad gradient value as the threshold,the missed selection rate was 25%;Under the HSV color model,the segmentation of tea sprouts was achieved by K-means clustering methods,and the segmentation accuracy in sunny and cloudy were 72.48% and 77.83%. Compared with the direct K-means segmentation method,the false positive rate are reduced 5.19% and 2.03%. The method could segment tea sprouts in natural environment effectively,reduce under-segmentation accuracy and over-segmentation rate,provide theoretical reference for intelligent picking.

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