基于CLAHE的苹果树树枝迭代阈值分割方法研究

Iterative Threshold Segmentation of Apple Branch Images Based on CLAHE

针对农业采摘机器人自主导航和采摘过程中的障碍物树枝识别问题,为解决迭代阈值分割算法在目标与背景图像灰度差别不明显情况下的分割缺陷,提出了基于对比度受限自适应直方图均衡化基础上的果树树枝迭代阈值分割方法。首先,通过颜色空间变换,将RGB颜色空间的果树树枝图像转换到XYZ和I1I2I3颜色空间,并提取出X-Y色差因子和I2颜色因子,对其进行灰度差别分析;然后,对灰度差别不明显的图像进行对比度受限直方图均衡化处理后,再进行迭代阈值分割,从而剥离出树枝区域。实验结果显示,采用本文方法,树枝图像分割成功率为92%。

For automatically navigating and identifying branches obstacle in the picking process of agricultural harvesting robots,it is necessary to solve the defection of iterative threshold segmentation since the gray scale difference between target and background is not clear.The iterative threshold segmentation of apple branch images based on contrast limited adaptive histogram equalization（CLAHE） was proposed.Firstly,the RGB color space of the apple branch images were transformed to the XYZ and I1I2I3color space by transformation,and the X-Y color difference factors and I2color factor of the apple branch images were extracted to analyze their gray level difference.Then the CLAHE was applied to the image in which the gray level difference was not obvious before iterative threshold.Finally,the apple branch images were segmented from the original images.Results showed that the ratio of successful segmentation was 92%.