OSA's Digital Library

Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 5, Iss. 1 — Jan. 10, 2007
  • pp: 25–27

A modified region growing algorithm for multi-colored image object segmentation

Yuxi Chen and Chongzhao Han  »View Author Affiliations


Chinese Optics Letters, Vol. 5, Issue 1, pp. 25-27 (2007)


View Full Text Article

Acrobat PDF (142 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations
  • Export Citation/Save Click for help

Abstract

A hybrid algorithm based on seeded region growing and k-means clustering was proposed to improve image object segmentation result. A user friendly segmentation tool was provided for the definition of objects, then k-means algorithm was utilized to cluster the selected points into k seeds-clusters, finally the seeded region growing algorithm was used for object segmentation. Experimental results show that the proposed method is suitable for segmentation of multi-colored object, while conventional seeded region growing methods can only segment uniform-colored object.

© 2007 Chinese Optics Letters

OCIS Codes
(100.2960) Image processing : Image analysis
(100.3010) Image processing : Image reconstruction techniques
(100.5010) Image processing : Pattern recognition

Citation
Yuxi Chen and Chongzhao Han, "A modified region growing algorithm for multi-colored image object segmentation," Chin. Opt. Lett. 5, 25-27 (2007)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-5-1-25


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. R. Adams and L. Bischof, IEEE Trans. Pattern Anal. Machine Intell. 16, 641 (1994).
  2. S.-Y. Wan and E. H. William, IEEE Trans. Image processing 12, 1007 (2003).
  3. J. Xu and P. Shi, Chin. Opt. Lett. 1, 645 (2003).
  4. S. Sun, D. R. Haynor, and Y. Kim, IEEE Trans. Circuits Syst. for Video Technol. 13, 75 (2003).
  5. B. A. Maxwell, Lecture Notes in Computer Science 1614, 517 (1999).
  6. S. Cagnoni, A. B. Dobrzeniecki, J. C. Yanch, and R. Poli, in Proceedings of IEEE International Conference on Image Processing 3, 498 (1994).
  7. D Comaniciu and V. Ramesh, IEEE Trans. Pattern Anal. Machine Intell. 25, 564 (2003).
  8. D. A. Clausi, Pattern Recognition 35, 1959 (2002).
  9. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, IEEE Trans. Pattern Anal. Machine Intell. 24, 881 (2002).
  10. C. Revol and M. Jourlin, Pattern Recognition Lett. 18, 249 (1997).
  11. Y. Feng, H. Fang, and J. Jiang, Lecture Notes in Computer Science 3687, 542 (2005).
  12. L. Zhi and Y. Jie, Electron. Lett. 40, 302 (2004).
  13. C.-H. Chung and W.-N. Lie, IEEE Trans. Image Proc. 13, 1379 (2004).
  14. F. B. Tek, A. G. Dempster, and I. Kale, Electron. Lett. 40, 1332 (2004).
  15. T. Huseyin and C. A. Huseyin, Lecture Notes in Computer Science 3216, 127 (2004).

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited