A State-of-the-Art Review with Code about Connected Components Labeling on GPUs

Abstract: This article is about Connected Components Labeling (CCL) algorithms developed for GPU accelerators. The task itself is employed in many modern image-processing pipelines and represents a fundamental step in different scenarios, whenever object recognition is required. For this reason, a strong effort in the development of many different proposals devoted to improving algorithm performance using different kinds of hardware accelerators has been made. This paper focuses on GPU-based algorithmic solutions published in the last two decades, highlighting their distinctive traits and the improvements they leverage. The state-of-the-art review proposed is equipped with the source code, which allows to straightforwardly reproduce all the algorithms in different experimental settings. A comprehensive evaluation on multiple environments is also provided, including different operating systems, compilers, and GPUs. Our assessments are performed by means of several tests, including real-case images and synthetically generated ones, highlighting the strengths and weaknesses of each proposal. Overall, the experimental results revealed that block-based oriented algorithms outperform all the other algorithmic solutions on both 2D images and 3D volumes, regardless of the selected environment.

Citation:

Bolelli, Federico; Allegretti, Stefano; Lumetti, Luca; Grana, Costantino "A State-of-the-Art Review with Code about Connected Components Labeling on GPUs" IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, pp. 1 -20 , 2024 DOI: 10.1109/TPDS.2024.3434357

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• DOI: 10.1109/TPDS.2024.3434357