Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지

Parallel Connected Component Labeling Based on the Selective Four Directional Label Search Using CUDA

  • Received : 2015.05.19
  • Accepted : 2015.08.08
  • Published : 2015.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

Connected component labeling (CCL) is a mandatory step in image segmentation where objects are extracted and uniquely labeled. CCL is a computationally expensive operation and thus is often done in parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method, modified 8 directional label selection (M8DLS) method, HYBRID1 method, and HYBRID2 method. Soh et al. showed that HYBRID2 outperforms the others and is the best so far. In this paper we propose a new hybrid parallel CCL algorithm termed as HYBRID3 that combines selective four directional label search (S4DLS) with label backtracking (LB). We show that the average percentage speedup of the proposed over M8DLS is around 60% more than that of HYBRID2 over M8DLS for various kinds of images.

Keywords

References

  1. K. Wu, E. Otoo, and K. Suzuki, "Optimizing two-pass connected-component labeling algorithms," Pattern Analysis & Applications vol. 12 issue 2, pp. 117-135, 2009. https://doi.org/10.1007/s10044-008-0109-y
  2. K. Suzuki, I. Horiba, and N. Sugie, "Linear-time connected-component labeling based on sequential local operations,"Computer Vision and Image Understanding vol. 89 issue 1, pp. 1-23, 2003. https://doi.org/10.1016/S1077-3142(02)00030-9
  3. R. Farber, CUDA Application Design and Development, Waltham: Elsevier, 2011.
  4. Y. Soh,H. Ashraf, and I. Kim, "An Improved Hybrid Approach to Parallel Connected Component Labeling using CUDA," Journal of the Institute of Signal Processing and Systems, Vol. 16, No. 1, pp.1-8, Jan. 2015.
  5. F. Chang, C. Chen, and C. Lu, "A linear-time Component-labeling algorithm using contour tracing technique,"Computer Vision and Image Understanding vol. 93 issue 2, pp. 206-220, 2004. https://doi.org/10.1016/j.cviu.2003.09.002
  6. O. Kalentov, A. Rai, S. Kemnitz, and R. Schneider, "Connected component labeling on a 2D grid using CUDA," J. Parallel Distributed Computing vol. 71, pp. 615-620, 2011. https://doi.org/10.1016/j.jpdc.2010.10.012
  7. Y. Soh, H. Ashraf, Y. Hae and I. Kim, "Fast Parallel Connected component labeling Algorithm in CUDA based on 8-Directional Label Selection," International Journal of Latest Research in Science and Technology, pp. 187-190, 2014.
  8. Y. Soh, H. Ashraf, Y. Hae and I. Kim, "A Hybrid Approach to Parallel Connected Component Labeling Using CUDA," International Journal of Signal Processing Systems, Vol. 1, No. 2, pp. 130-135, 2013.
  9. K. Hawick, A. Leist, and D. Playne, "Parallel graph component labeling with GPUs and CUDA," Parallel Computing vol. 36 issue 12, 2010.
  10. Y. Soh, H. Ashraf, Y. Hae and I. Kim," A Simple and Fast parallel Connected Component Labeling using CUDA," in Proceedings of International Conference on Computer Applications and Information Processing Technology, pp. 61-64, 2013.