Ultimate levelings are operators that extract important image contrast information from a scale-space based on levelings. Along with the residual extraction process, some residues usually come from undesirable regions, and they should be filtered out. For this, some strategies can be applied to filter these undesirable residues. In this paper, we introduce a new approach to detect desirable regions from ultimate levelings through a new hierarchical structure called residual tree. From this structure, we extract attribute vectors to build a machine learning model which gives a matching value between ground truth regions and residual tree nodes. Thus, from the selected residual tree nodes, we present a new approach to choose the best disjoint residual nodes which gives the regions of the ultimate levelings. Finally, from the ultimate levelings, we use its partition associated function to solve the segmentation problem. In order to evaluate our new approach, some experiments were carried out with a plant dataset and results report the state-of-the-art performance in plant segmentation.

最终水准仪是根据水准仪从标尺空间中提取重要图像对比度信息的操作员。随着残留物的提取过程，一些残留物通常来自不希望的区域，应将其过滤掉。为此，可以应用一些策略来过滤这些不希望的残留物。在本文中，我们介绍了一种新方法，可以通过称为残差树的新层次结构从最终水平中检测出所需区域。从这种结构中，我们提取属性向量以构建机器学习模型，该模型给出地面真实区域和残差树节点之间的匹配值。因此，从选定的残差树节点中，我们提出了一种选择最佳不相交残差节点的新方法，该方法给出了最终水准的区域。最后，从最终的水平 我们使用其分区关联函数来解决分割问题。为了评估我们的新方法，对植物数据集进行了一些实验，结果报告了植物分割中的最新性能。