Multiple circle detection based on center-based clustering

Rudolf Scitovski

doi:10.1016/j.patrec.2014.09.010

2015-01-01

PRE

Rudolf Scitovski

Tomislav Marošević *

DOI：10.1016/j.patrec.2014.09.010

原文链接

原文求助

摘要

En 中文

The multiple circle detection problem has been considered in the paper on the basis of given data point set A subset of R-2. It is supposed that all data points from the set A come from k circles that should be reconstructed or detected. The problem has been solved by the application of center-based clustering of the set A, i.e. an optimal k-partition is searched for, whose clusters are determined by corresponding circle-centers. Thereby, the algebraic distance from a point to the circle is used. First, an adaptation of the well-known k-means algorithm is given in the paper. Also, the incremental algorithm for searching for an approximate globally optimal k-partition is proposed. The algorithm locates either a globally optimal k-partition or a locally optimal k-partition close to the global one. Since optimal partitions with 2, 3,... clusters are determined successively in the algorithm, several well-known indexes for determining an appropriate number of clusters in a partition are adopted for this case. Thereby, the Hausdorff distance between two circles is used and adopted. The proposed method and algorithm are illustrated and tested on several numerical examples. (C) 2014 Elsevier By. All rights reserved.

Keyword:

Multiple circle detection

Center based clustering

Globally optimal partition

Approximate optimization

DIRECT