In this paper we propose a method to detect concave points as a first step to segment overlapped objects on images. Given an image of an object cluster we compute the curvature on each point of its contour. Then, we select regions with the highest probability to contain an interest point, that is, regions with higher curvature. Finally we obtain an interest point from each region and we classify them between convex and concave. In order to evaluate the quality of the concave point detection algorithm we constructed a synthetic dataset to simulate overlapping objects, providing the position of the concave points as a ground truth. As a case study, the performance of a well-known application is evaluated, such as the splitting of overlapped cells in images of peripheral blood smears samples of patients with sickle cell anaemia. We used the proposed method to detect the concave points in clusters of cells and then we separate this clusters by ellipse fitting. Experimentally we demonstrate that our proposal has a better performance than the state-of-the-art.

翻译：在本文中,我们建议了一种方法来检测曲线点,作为在图像上分割重叠对象的第一步。根据物体群集的图像,我们计算出其轮廓的轮廓。然后,我们选择最有可能包含一个利益点的区域,即曲线较高的区域。最后,我们从每个区域获得一个利益点,并将它们分为曲线和曲线。为了评估曲线点探测算法的质量,我们为模拟重叠对象构建了一个合成数据集,提供曲线点的位置,作为地面真相。作为案例研究,我们评估了众所周知的应用的性能,例如将重叠细胞分解到有镰状细胞贫血病人的边缘血涂片样本的图像中。我们使用拟议方法检测细胞群中的曲线点,然后用椭圆来分离这些组。我们实验性地证明我们的提议比艺术状态要好。