基于HSV空间再生稻植株与土壤背景图像分割

针对再生稻收割机视觉导航的稻田图像分割问题,结合再生稻植株的生长特点和再生稻避莊的要求,利用相机于农田采集再生稻图片,结合RGB、HSV、YCr Cb空间中的常用灰度化因子,进行灰度化对比试验并分析其直方图特征,得出在HSV空间的S分量灰度化;采用最大类间方差法(Otsu)得到初步分割阈值T,经进一步分析为保留较完整的不同成熟度再生稻植株特征,加入修正因子-a得到阈值T-a对图像二值化;再通过数学形态学,面积法过滤等后续处理,形成收割机行走的左右边界区域。结果表明:处理1副像素419×310的图像平均耗时0.053 s,可满足今后的实时性要求,分割出的图像基本上反应了再生稻植株的走势特征,与人眼判断植株边缘位置基本相符合。

Image Segmentation for Identifying Ratooning Rice Area from Soil Background Based on HSV Space

For the issue of image segmentation in the ratooning rice field to the vision navigation of the harvester , com-bined with the growth characteristics of the plant and the requirement to avoid ratooning rice , a method to the image was proposed .Taking the picture from field environment , combained with the commonly gray scale factor in the RGB、HSV、YCrCb color space , the contrast testing about graying image was took , and the histogram features was analysised .It was varianted in S characteristic by HSV space , and combined with the Otsu to get the initial segmentation threshold T .In or-der to maintain the integrity of the characteristics in the ratooning rice area with different maturity , the modified factor -awas added to get the segmentation threshold T-a.Then the graying image was binarized by the modified threshold , and used the mathematical morphology , filtered the small area with other subsequent process .Finally , the left and right boundary region to the harvester was formed .The results demonstrate that the average time of processing a 419 ×310 pixel image was 0 .053 s and met the need of real-time in the future .The segmented image basically reflected the trend charac-teristics of the ratooning rice area , which was consistent with the human vision to identify the edge position of the plant .