Recognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.

在计算机视觉领域的许多应用中，需要识别重叠的对象。示例包括细胞分割，气泡检测和血迹模式分析。本文提出了一种通过用椭圆近似重叠对象来识别重叠对象的方法。该方法旨在应用于被认为由一个或多个重叠物体组成的复杂形状区域。该方法有两个主要步骤。首先，通过对压缩图像进行欧几里德距离变换来生成候选椭圆池，并通过覆盖方法对该池进行滤波。其次，通过多边形逼近提取感兴趣区域轮廓上的凹点，以将轮廓划分为多个段。然后，通过选择最适合所识别段的最小子集，从候选项中选择最佳椭圆。我们建议使用通常用于聚类的调整后的兰德指数，以将拟合结果与地面真实情况进行比较。通过一组计算和优化效率，我们能够将我们的方法应用于由多个重叠区域组成的复杂图像中。在合成数据集，两种类型的细胞图像和血迹图案上的实验结果表明，与其他方法相比，我们的方法在椭圆识别中具有更高的准确性和灵活性。我们能够在包含多个重叠区域的复杂图像中应用我们的方法。在合成数据集，两种类型的细胞图像和血迹图案上的实验结果表明，与其他方法相比，我们的方法在椭圆识别中具有更高的准确性和灵活性。我们能够在包含多个重叠区域的复杂图像中应用我们的方法。在合成数据集，两种类型的细胞图像和血迹图案上的实验结果表明，与其他方法相比，我们的方法在椭圆识别中具有更高的准确性和灵活性。