A Parallel Implementation for Computing the Region-Adjacency-Tree of a Segmentation of a 2D Digital Image

  • Fernando Díaz-del-Río
  • Pedro Real
  • Darian Onchis
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9555)

Abstract

A design and implementation of a parallel algorithm for computing the Region-Adjacency Tree of a given segmentation of a 2D digital image is given. The technique is based on a suitable distributed use of the algorithm for computing a Homological Spanning Forest (HSF) structure for each connected region of the segmentation and a classical geometric algorithm for determining inclusion between regions. The results show that this technique scales very well when executed in a multicore processor.

Keywords

Digital image Segmentation RAG Parallel algorithm 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The first author gratefully acknowledges the support of the Spanish Ministry of Science and Innovation (project Biosense, TEC2012-37868-C04-02), the second author the support of the V Plan Propio de la Universidad de Sevilla, project number 2014/753, and the last author the support of the Austrian Science Fund(FWF): project number P27516.

References

  1. 1.
    Barbu, A., Zhu, S.-C.: Graph partitioning by Swendsen-Wang cuts. In: Ninth IEEE International Conference on Computer Vision, Nice, France, vol. 1, pp. 320–329 (2003)Google Scholar
  2. 2.
    Cucchiara, R., Grana, C., Prati, A., Seidenari, S., Pellacani, G.: Building the topological tree by recursive FCM color clustering. In: Proceeding International Conference on Pattern Recognition, vol. 116(1), pp. 759–762 (2002)Google Scholar
  3. 3.
    Cohn, A., Bennett, B., Gooday, J., Gotts, N.: Qualitative spacial representation and reasoning with the region connection calculus. GeoInformatica 1(3), 275–316 (1997)CrossRefGoogle Scholar
  4. 4.
    Costanza, E., Robinson, J.: A region adjacency tree approach to the detection and design of fiducials. In: Video, Vision and Graphics, pp. 63–99 (2003)Google Scholar
  5. 5.
    CUDA C Best Practices Guide Version. NVIDIA. http://developer.nvidia.com/. Accessed 14 July 2015
  6. 6.
    Damiand, G., Bertrand, Y., Fiorio, C.: Topological model for two-dimensional image representation: definition and optimal extraction algorithm. Comput. Vis. Image Underst. 93(2), 111–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Díaz-del-Río, F., Real, P., Onchis, D.: A parallel homological spanning forest framework for 2D topological image analysis. Submitted to Pattern Recognition Letters, July 2015Google Scholar
  8. 8.
    Duarte, A., Sánchez, A., Fernandez, F., Montemayor, A.S.: Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic. Pattern Recogn. Lett. 27, 1239–1251 (2006)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  10. 10.
    Gothandaraman, A.: Hierarchical image segmentation using the watershed algorithm with a streaming implementation. Ph.D. thesis, University of Tennessee, USA (2004)Google Scholar
  11. 11.
    Gupta, S., Palsetia, D., Patwary, M.M.A., Agrawal, A., Choudhary, A.N.: A new parallel algorithm for two-pass connected component labeling. In: 2014 IEEE International Parallel and Distributed Processing Symposium Workshop, pp. 1355–1362 (2014)Google Scholar
  12. 12.
    Haris, K., et al.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7(12), 1684–1699 (1998)CrossRefGoogle Scholar
  13. 13.
    Hernández, S.E., Barner, K.E.: Joint region merging criteria for watershed-based image segmentation. In: International Conference on Image Processing, vol. 2, pp. 108–111 (2000)Google Scholar
  14. 14.
    Hormann, K., Agathos, A.: The point in polygon problem for arbitrary polygons. Comput. Geom. 20(3), 131–144 (2001)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Kalentev, O., Rai, A., Kemnitz, S., Schneider, R.: Connected component labeling on a 2D grid using CUDA. J. Parallel Distrib. Comput. 71(4), 615–620 (2011)CrossRefGoogle Scholar
  16. 16.
    Molina-Abril, H., Real, P.: Homological spanning forest framework for 2D image analysis. Ann. Math. Artif. Intell. 4(64), 385–409 (2012)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Emphysema, H., E. Commons.wikimedia.org File: Emphysema H and E.jpg. Accessed 14 September 2015Google Scholar
  18. 18.
    Pavlidis, T.: Structural Pattern Recognition. Springer, New York (1977)CrossRefMATHGoogle Scholar
  19. 19.
    Sonka, M., et al.: Image Processing, Analysis and Machine Vision, 2nd edn. PWS, Pacific Grove (1999)Google Scholar
  20. 20.
    Rosenfeld, A.: Adjacency in digital pictures. Inf. Control 26, 24–33 (1974)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Orlando (1982)MATHGoogle Scholar
  22. 22.
    Sarkar, A., Biswas, M.K., Sharma, K.M.S.: A simple unsupervised MRF model based image segmentation approach. IEEE Trans. Image Process. 9(5), 801–811 (2000)CrossRefGoogle Scholar
  23. 23.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intel. 22(8), 888–905 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fernando Díaz-del-Río
    • 1
  • Pedro Real
    • 1
  • Darian Onchis
    • 2
  1. 1.H.T.S. Informatics’ EngineeringUniversity of SevilleSevilleSpain
  2. 2.Faculty of MathematicsUniversity of ViennaViennaAustria

Personalised recommendations