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当前位置: 首页 > 文章 > 基于SLIC超像素的茶叶嫩芽图像分割方法研究 西昌学院学报(自然科学版) 2019 (4) 75-77+124
Position: Home > Articles > Study on the Method of Image Segmentation of Tea Sprouts Based on SLIC Super-pixel Journal of Xichang University(Natural Science Edition) 2019 (4) 75-77+124

基于SLIC超像素的茶叶嫩芽图像分割方法研究

作  者：
夏华鹍;方梦瑞;黄涛;吕军
单  位：
浙江理工大学信息学院;黄山学院信息工程学院
关键词：
茶叶嫩芽;超像素;简单线性迭代聚类;图像分割
摘  要：
准确识别茶叶嫩芽是实现茶叶智能采摘的前提。针对自然环境下的茶叶嫩芽图像分割受天气、光照等因素影响较大,提出基于SLIC超像素的嫩芽分割方法。提取R、G、B、H、S、V、Y、Cb、Cr、超红、超绿、Cg、R-B、G-B共14个颜色分量,分析发现以超红、Cg和G-B三分量合成彩图中嫩芽与背景对比度较大,作为待分割对象图;利用SLIC超像素分割算法获取超像素块,并对每个超像素块提取平均横坐标、平均纵坐标、平均超红、平均Cg、平均G-B 5个特征作为分割依据;利用阈值分割、小目标去除、填充和"逻辑与"等操作,得到茶叶嫩芽彩色分割图。对不同地域、不同环境下嫩芽图像进行实验表明,基于SLIC超像素的嫩芽分割平均分割精度达75.6%,较传统G-B阈值分割平均精确度高16.6%。该方法不仅能抑制光照等因素对茶叶图像的影响,还能有效分割茶叶嫩芽,鲁棒性较强。
译  名：
Study on the Method of Image Segmentation of Tea Sprouts Based on SLIC Super-pixel
作  者：
XIA Huakun;FANG Mengrui;HUANG Tao;LV Jun;School of Information Engineering, Huangshan University;School of Information Science and Technology, Zhejiang Sci-tech Universtiy;
关键词：
tea sprouts;;super-pixel;;simple linear iterative clustering;;image segmentation
摘  要：
Accurate identification of tea sprouts is a prerequisite to intelligent tea picking. As the image segmentation of tea sprouts under natural environment is greatly affected by weather, light and other factors, we proposed a segmentation method for tea sprouts based on SLIC super-pixel. Fourteen color components(R, G, B, H,S, V, Y, Cb, Cr, super-red, super-green, Cg, r-b, g-b) were extracted. The SLIC super-pixel segmentation algorithm was used to obtain the super-pixel blocks, and the average abscissa, average ordinate, average super-red, average Cg and average g-b were extracted from each super pixel block as the segmentation basis. Threshold segmentation,small target removal, filling and"logic and"operations was used to get the color images of tea. Experiments showed that the average accuracy of tea sprouts segmentation in different regions and environments based on SLIC super-pixel reached 75.6%, which was 16.6% higher than that of traditional G-B threshold segmentation. This method can not only counter the effect of light and other factors on tea images, but also effectively segment tea sprouts with robustness.

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