Motivated by the problem of identifying rod-shaped particles (e.g. bacilliform bacterium), in this paper we consider the multiple generalized circle detection problem. We propose a method for solving this problem that is based on center-based clustering, where cluster-centers are generalized circles. An efficient algorithm is proposed which is based on a modification of the well-known k-means algorithm for generalized circles as cluster-centers. In doing so, it is extremely important to have a good initial approximation. For the purpose of recognizing detected generalized circles, a QAD-indicator is proposed. Also a new DBC-index is proposed, which is specialized for such situations. The recognition process is intitiated by searching for a good initial partition using the DBSCAN-algorithm. If QAD-indicator shows that generalized circle-cluster-center does not recognize searched generalized circle for some cluster, the procedure continues searching for corresponding initial generalized circles for these clusters using the Incremental algorithm. After that, corresponding generalized circle-cluster-centers are calculated for obtained clusters. This will happen if a data point set stems from intersected or touching generalized circles. The method is illustrated and tested on different artificial data sets coming from a number of generalized circles and real images.

出于识别棒状颗粒（例如杆菌形细菌）的问题，本文考虑多重广义圆检测问题。我们提出了一种基于中心聚类的解决此问题的方法，其中聚类中心是广义圆。提出了一种有效的算法，该算法基于对众所周知的将广义圆作为聚类中心的k均值算法的改进。在此过程中，具有良好的初始近似值非常重要。为了识别检测到的广义圆，提出了一种QAD指标。也是新的DBC建议使用-index，它专门用于这种情况。通过使用DBSCAN算法搜索良好的初始分区来启动识别过程。如果QAD指示器显示广义圆簇中心无法识别某个聚类的搜索广义圆，则该过程将继续使用增量算法为这些聚类搜索相应的初始广义圆。此后，为获得的聚类计算相应的广义圆聚类中心。如果数据点集源自相交或接触的广义圆，则会发生这种情况。该方法在来自多个广义圆和真实图像的不同人工数据集上进行了说明和测试。