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Recognition of overlapping elliptical objects in a binary image

机译：识别二值图像中的重叠椭圆对象

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摘要

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.
机译：在计算机视野领域的许多应用程序中需要识别重叠对象。实例包括细胞分段，泡沫检测和血迹模式分析。本文介绍了一种通过用椭圆近似地识别重叠对象的方法。该方法旨在应用于复杂形状的区域，被认为由一个或多个重叠物体组成。该方法有两个主要步骤。首先，通过在压缩图像上施加欧几里德距离变换来生成候选椭圆池，并且通过覆盖方法滤波池。其次，通过多边形近似提取感兴趣区域的轮廓上的凹点，以将轮廓划分为段。然后，通过选择最佳拟合识别的段的最小子集中，从候选中选择最佳椭圆。我们建议使用调整后的兰特指数，通常应用于聚类，以将拟合结果与地面真相进行比较。通过一组计算和优化效率，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。实验结果对合成数据集，两种类型的细胞图像和血迹图案显示了我们在椭圆形识别中的卓越准确性和灵活性，相对于其他方法。

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