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Efficient Graph-Based Volumetric Segmentation

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Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

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Abstract

The emergence and increasing importance of digital society increased the role of software applications in smart environments. Associated with these paradigms are a multitude of applications that generate and require analysis of massive volumes of diverse, heterogeneous, complex, and distributed data. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. There is a wide range of computational vision problems for 2D images that could use of segmented images. However the problems of 3D image segmentation and grouping remain great challenges for computer vision. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. Many approaches aim to create large regions using simple homogeneity criteria based only on color or texture. However, 3D applications for such approaches are limited as they often fail to create meaningful partitions due to the computation complexity. We are introducing new algorithm for spatial segmentation based on Virtual Tree-Hexagonal Structure constructed on the image voxels. Then the paper depicts a Spatial Segmentation Algorithm. Spatial Segmentation Algorithm contains many other algorithms but only Color-based segmentation algorithm is presented based on the limited space of paper. Then the paper describes the Computational Complexity Analysis of the Color-Based Spatial Segmentation Algorithm.

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References

  1. Janakiraman, T., Mouli, P.C.: Image segmentation using euler graphs. Int. J. Comput. Commun. Control 5(3), 314–324 (2010)

    Google Scholar 

  2. Felzenszwalb, P., Huttenlocher, W.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167–181 (2004)

    Article  Google Scholar 

  3. Guigues, L., Herve, L., Cocquerez, L.P.: The hierarchy of the cocoons of a graph and its application to image segmentation. Pattern Recogn. Lett. 24(8), 1059–1066 (2003)

    Article  MATH  Google Scholar 

  4. Gdalyahu, Y., Weinshall, D., Werman, M.: Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans. Pattern Anal. Mach. Intell. 3(10), 1053–1074 (2001)

    Article  Google Scholar 

  5. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 885–905 (2000)

    Google Scholar 

  6. Camilus, K.S., Govindan, V.: A review on graph based segmentation. Int. J. Image Graph. Sig. Proc. 5, 1–13 (2012)

    Article  Google Scholar 

  7. Jermyn, I., Ishikawa, H.: Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 1075–1088 (2001)

    Article  Google Scholar 

  8. Cooper, M.: The tractibility of segmentation and scene analysis. Int. J. Comput. Vision 30(1), 27–42 (1998)

    Article  Google Scholar 

  9. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vision 43(1), 7–27 (2001)

    Article  MATH  Google Scholar 

  10. Comaniciu, D., Meer, P.: Robust analysis of feature spaces: color image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  11. Brezovan, M., Burdescu, D., Ganea, E., Stanescu, L.: An adaptive method for efficient detection of salient visual object from color images. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 2345–2349. Istanbul, Turkey (2010)

    Google Scholar 

  12. Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1197–1203. Madison, Wisconsin (1999)

    Google Scholar 

  13. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

    MathSciNet  Google Scholar 

  14. Burdescu, D., Brezovan, M., Ganea, E., Stanescu, L.: A new method for segmentation of images represented in a HSV color space. In: Proceedings of the Advanced Concepts for Intelligent Vision Systems Conference, pp. 606–617 (2009)

    Google Scholar 

  15. Stanescu, L., Burdescu, D., Brezovan, M.: A comparative study of some methods for color medical images segmentation. EURASIP Journal on Advances in Signal Processing, 128 (2011)

    Google Scholar 

  16. Stanescu, L., Burdescu, D., Brezovan, M., Mihai, G.: Creating New Medical Ontologies for Image Annotation. Springer, Berlin (2011)

    Google Scholar 

  17. Gonzales, R., Wintz, P.: Digital Image Processing. Addison-Wesley, Reading (1987)

    Google Scholar 

  18. Middleton, L., Sivaswamy, J.: Hexagonal Image Processing; A Practical Approach. Advances in Pattern Recognition. Springer, Berlin (2005)

    Google Scholar 

  19. Cormen, T., Leiserson, C., Rivest, R.: Introduction to Algorithms. MIT Press, Cambridge (1990)

    MATH  Google Scholar 

  20. Gijsenij, A., Gevers, T., Lucassen, M.P.: A perceptual comparison of distance measures for color constancy algorithms. In: Proceedings of the 10th European Conference on Computer Vision, pp. 208–221. Marseille, France (2008)

    Google Scholar 

  21. Sanfeliu, A., Alquézar, R., Andrade, J., Climent, J., Serratosa, F., Verges, J.: Graph-based representations and techniques for image processing and image analysis. Pattern Recogn. 35(3), 639–650 (2001)

    Article  Google Scholar 

  22. Burdescu, D.D., Brezovan, M., Stanescu, L., Spahiu, C.S.: A spatial segmentation method. Int. J. Comput. Sci. Appl. 1(5), 75–100 (2014)

    Google Scholar 

  23. Burdescu, D.D., Stanescu, L., Brezovan, M., Spahiu, C.S.: Computational complexity analysis of the graph extraction algorithm for 3d segmentation. In: Proceedings of the IEEE Tenth World Congress on Services, pp. 462–470. Alaska, USA (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Craiova, Bd. Decebal 107, Craiova, Romania

    Dumitru Dan Burdescu, Marius Brezovan, Liana Stanescu & Cosmin Stoica Spahiu

Authors
  1. Dumitru Dan Burdescu
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  2. Marius Brezovan
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  3. Liana Stanescu
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  4. Cosmin Stoica Spahiu
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Corresponding author

Correspondence to Dumitru Dan Burdescu .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

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© 2015 Springer International Publishing Switzerland

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Burdescu, D.D., Brezovan, M., Stanescu, L., Spahiu, C.S. (2015). Efficient Graph-Based Volumetric Segmentation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14653-9

  • Online ISBN: 978-3-319-14654-6

  • eBook Packages: EngineeringEngineering (R0)

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