基于多阈值算法的包装箱内香蕉区域的图像分割方法

A multiple-threshold based image segmentation method for bananas in the crate

采用基于阈值技术的自动分割算法实现香蕉包装箱图像的分割。在手动分割的区域内离散选取香蕉和背景区域的像素点,并分别提取RGB,HSV和CIE L*a*b*3个色彩空间内共计9个颜色特征值,通过统计分析确定使用3个分别来自B,L*和b*分量的阈值进行自动分割算法的设计。采用直观对比的方法进行定性评价,其结果显示,手动和自动分割区域的轮廓基本相似;采用面积比作为指标进行定量评价,其结果显示,该算法对10个测试样本的平均面积比为80%以上。测试结果表明,该自动分割算法对香蕉包装箱图像的总体分割效果良好,可以为香蕉催熟房实时品质监控系统的实现提供关键技术。

In this study,a threshold-based algorithm was designed for image segmentation of bananas in the crate.The pixels of banana and background were selected discretely in the manual-segmented regions. Subsequently,nine color features were extracted from RGB,HSV and CIE L＊a＊b＊color spaces. Three thresholds from B,L＊and b＊color channels were used for the design of the algorithm,which were determined by the statistical analysis. The qualitative assessment of segmentation performance was evaluated by the application of intuitive comparison,and the obtained results showed that the outlines of manual-segmented and automatic-segmented regions were almost similar.Simultaneously,the quantitative evaluation of segmentation performance was carried out by area ratio,and the average area ratio of ten tested samples was beyond 80%. These two tested results indicated that the performance of this automatic segmentation algorithm might be satisfying,and this methodology could provide the key technology for the design and achievement of the real-time quality monitoring and control system in banana ripening rooms.