An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs
This work addresses a domain-specific problem in image processing by providing an incremental improvement for faster connected components labeling on GPUs.
The paper tackled the problem of efficiently labeling connected components in 2D images by proposing an optimized union-find algorithm for GPUs, resulting in a speedup of over 1.3X in average running time.
In this paper, we report an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing the GPU architecture. The proposed method contains three phases: UF-based local merge, boundary analysis, and link. The coarse labeling in local merge reduces the number atomic operations, while the boundary analysis only manages the pixels on the boundary of each block. Evaluation results showed that the proposed algorithm speed up the average running time by more than 1.3X.