Efficient Parallel Connected Component Labeling With a Coarse-to-Fine Strategy

This paper proposes a new parallel approach to solve connected components on a 2-D binary image. The following strategies are employed to accelerate neighborhood exploration after dividing an input image into independent blocks: 1) in the local labeling stage, a coarse-labeling algorithm, including row-column connection and unification, is applied first to reduce the complexity of an initialized local label map; a refinement algorithm is then introduced to merge separated sub-regions from a single component; and 2) in the block merge stage, we scan the pixels on the block boundary instead of solving the connectivity of all the pixels. With the proposed method, the length of label-equivalence lists in both the local labeling stage and global labeling stage are compressed and the number of memory accesses is reduced. Thus, the efficiency of connected component labeling is improved. The proposed strategies are illustrated using 4-neighbor connectivity, and the case of 8-neighbor connectivity is also discussed. The YACCLAB data sets, including both synthetic and real images, are used to evaluate the new algorithm and compare it to existing algorithms. The comparative results show that the proposed new algorithm outperforms the other approaches in both the 4-neighbor connectivity and 8-neighbor connectivity cases.