Optimized Block-Based Algorithms to Label Connected Components on GPUs

Stefano Allegretti, Federico Bolelli, Costantino Grana

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