Optimized Block-Based Algorithms to Label Connected Components on GPUs

Abstract

Connected Components Labeling (CCL) is a crucial step of several image processing and computer vision pipelines. Many efficient sequential strategies exist, among which one of the most effective is the use of a block-based mask to drastically cut the number of memory accesses. In the last decade, aided by the fast development of Graphics Processing Units (GPUs), a lot of data parallel CCL algorithms have been proposed along with sequential ones. Applications that entirely run in GPU can benefit from parallel implementations of CCL that allow to avoid expensive memory transfers between host and device. In this paper, two new eight-connectivity CCL algorithms are proposed, namely Block-based Union Find (BUF) and Block-based Komura Equivalence (BKE). These algorithms optimize existing GPU solutions introducing a block-based approach. Extensions for three-dimensional datasets are also discussed. In order to produce a fair comparison with previously proposed alternatives, YACCLAB, a public CCL benchmarking framework, has been extended and made suitable for evaluating also GPU algorithms. Moreover, three-dimensional datasets have been added to its collection. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposals with respect to state-of-the-art, both on 2D and 3D scenarios.