On the Performance of Connected Components Grouping

Grouping processes may benefit computationally when simple algorithms are used as part of the grouping process. In this paper we consider a common and extremely fast grouping process based on the connected components algorithm. Relying on a probabilistic model, we focus on analyzing the algorithm's performance. In particular, we derive the expected number of addition errors and the group fragmentation rate. We show that these performance figures depend on a few inherent and intuitive parameters. Furthermore, we show that it is possible to control the grouping process so that the performance may be chosen within the bounds of a given tradeoff. The analytic results are supported by implementing the algorithm and testing it on synthetic and real images.