This paper introduces a novel method for segmentation of clustered partially overlapping convex objects in silhouette images. The proposed method involves three main steps: pre-processing, contour evidence extraction, and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image by detecting concave points. After this the contour segments which belong to the same objects are grouped. The grouping is formulated as a combinatorial optimization problem and solved using the branch and bound algorithm. Finally, the full contours of the objects are estimated by a Gaussian process regression method. The experiments on a challenging dataset consisting of nanoparticles demonstrate that the proposed method outperforms three current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have a convex shape.

本文介绍了一种新的分割轮廓图像中部分重叠的凸对象的新方法。所提出的方法包括三个主要步骤：预处理，轮廓证据提取和轮廓估计。轮廓证据提取通过检测凹点从二值化图像中恢复轮廓段开始。之后，将属于相同对象的轮廓线段分组。将分组表述为组合优化问题，并使用分支定界算法进行求解。最后，通过高斯过程回归方法估计对象的整个轮廓。在由纳米颗粒组成的具有挑战性的数据集上进行的实验表明，该方法在重叠的凸形物体分割中优于三种当前的最新技术。