JSTS:Journal of Semiconductor Technology and Science

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Block-Based Predictive Watershed Transform for Parallel Video Segmentation

  • Jang, Jung-Whan (Inter-university Semiconductor Research Center, Department of Electrical Engineering, Seoul National University) ;
  • Lee, Hyuk-Jae (Inter-university Semiconductor Research Center, Department of Electrical Engineering, Seoul National University)
  • Received : 2011.08.31
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Predictive watershed transform is a popular object segmentation algorithm which achieves a speed-up by identifying image regions that are different from the previous frame and performing object segmentation only for those regions. However, incorrect segmentation is often generated by the predictive watershed transform which uses only local information in merge-split decision on boundary regions. This paper improves the predictive watershed transform to increase the accuracy of segmentation results by using the additional information about the root of boundary regions. Furthermore, the proposed algorithm is processed in a block-based manner such that an image frame is decomposed into blocks and each block is processed independently of the other blocks. The block-based approach makes it easy to implement the algorithm in hardware and also permits an extension for parallel execution. Experimental results show that the proposed watershed transform produces more accurate segmentation results than the predictive watershed transform.

Keywords

References

  1. J.B.T.M. Roerdink and A. Meijster, "The Watershed Transform: Definitions, Algorithms and Parallelization Strategies," Fundamenta Informaticae, Vol.41, nos. 1-2, pp.187-228, 2001.
  2. R. Gonzalez and R. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2007.
  3. Y. Tsai, C. Lai, Y. Hung, and Z.Shih, "A Bayesian approach to video object segmentation via merging 3-D watershed volumes," IEEE Trans. on Circuits and Systems for Video Technology, Vol.15, No.1, pp.175- 180, Jan., 2005. https://doi.org/10.1109/TCSVT.2004.839973
  4. H. Xu, A. A. Younis, and M. R. Kabuka, "Automatic moving object extraction for contentbased applications," IEEE Trans. on Circuits and Systems for Video Technology, Vol.14, No.6, pp.796-812, Jun., 2004. https://doi.org/10.1109/TCSVT.2004.828338
  5. S. Chien, Y. Huang, B. Hsieh, S. Ma, and L. Chen, "Fast video segmentation algorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques," IEEE Trans. on Multimedia, Vol.6, No.5, pp.732-748, Oct., 2004. https://doi.org/10.1109/TMM.2004.834868
  6. A.N. Moga and M. Gabbouj, "Parallel image component labelling with watershed transformation ," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.5, pp.441-450, May, 1997. https://doi.org/10.1109/34.589204
  7. A. Moga, B. Cramariuc, and M. Gabbouj, "An Efficient Watershed Segmentation Algorithm Suitable for Parallel Implementation," in Proc. of IEEE Int'l Conf. Image Processing, Vol.2, No., pp.101-104 Oct., 1995.
  8. S.-Y. Chien, Y.-W. Huang, and L.-G. Chen, "Predictive watershed: a fast watershed algorithm for video segmentation," IEEE Trans. on Circuits and Systems for Video Technology, Vol.13, No.5, pp.453-461, May, 2003. https://doi.org/10.1109/TCSVT.2003.811605
  9. B. J. Mealy, "Scanning Order Dependencies in Watershed Transform", Technical Report UCSCCRL-02-37, Dec., 2002.
  10. L. Vincent, and P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.13, No.6, pp.583-598, Jun., 1991. https://doi.org/10.1109/34.87344
  11. J. S. Kim, H. J. Lee, T. H. Lee, M. J. Cho, and J. B. Lee, "Hardware/Software Partitioned Implementation of Real-time Object-oriented Camera for Arbitraryshaped MPEG-4 Contents," in Proc. Of IEEE/ ACM/IFIP Workshop on Embedded Systems for Real Time Multimedia, pp.7-12, Oct., 2006.
  12. J. S. Kim, J. H Zhu, and H. J. Lee, "Block-Level Processing of a Video Object Segmentation Algorithm for Real-Time Systems," in Proc. Of IEEE International Conference on Multimedia and Expo, pp.2066-2069, 2-5 Jul., 2007.
  13. N. Hirai, T. Song, Y. Liu, and T. Shimamoto, "A Novel Spiral-Type Motion Estimation Architecture for H.264/AVC," Journal of Semiconductor Technology and Science, Vol.10, No.1, pp.37-44, Mar., 2010. https://doi.org/10.5573/JSTS.2010.10.1.037