Skip to main content
SpringerLink
Log in

A hybrid method for range image segmentation

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

This paper presents a hybrid algorithm that combines edge detection and region growing approaches to range image segmentation. Edges detected by this method possess good localization properties. This aids in steering the region growing process towards accurate border partitioning. In addition, the incorporation of the region growing process eliminates internal microedges and provides for missing border reconstruction because it is able to detect weak edges. It is believed that the edge and segmentation maps produced may prove to be valuable for CAD-based modeling purposes and may also help to provide a descriptive syntax at the object identification level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. C.M. Bastuschek, “Techniques for real-time generation of range images,” inProc. IEEE Conference on Computer Vision and Pattern Recognition, 1989, pp. 262–268.

  2. M. Rioux, F. Blais, J.A. Beraldin, and P. Boulanger, “Range imaging sensors development at NCR laboratories,” inProc. IEEE Workshop on Interpretation of 3D Scenes, November 1989, pp. 154–160.

  3. R.C. Jain and A.K. Jain, “Report on range image understanding workshop, East Lansing, Michigan, March 21–23, 1988,” inAnalysis and Interpretation of Range Images, R.C. Jain and A.K. Jain, eds., Springer-Verlag: New York, 1990, pp. 1–31.

    Google Scholar 

  4. D. Terzopolous, “The computation of visible-surface representations,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-10, pp. 417–438, 1988.

    Google Scholar 

  5. D. Terzopolous and D. Metaxas, “Dynamic 3D models with local and global deformation: Deformable superquadrics,”IEEE Trans. Patt. Anal. Mach. Intell., vol. 13, pp. 703–714, 1991.

    Google Scholar 

  6. A. Pentland and S. Sclaroff, “Closed-form solutions for physically based shape modelling and recognition,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-13, pp. 715–729, 1991.

    Google Scholar 

  7. R. Nevatia and K.R. Babu, “Linear feature extraction and description,”Comput. Graph. Image Process., vol. 13, pp. 257–269, 1980.

    Google Scholar 

  8. B.A. Parvin and B. Bhanu, “Segmentation of images using a relaxation technique,” inProc. IEEE Conference on Computer Vision and Pattern Recognition, 1983, pp. 151–153.

  9. S. Basu, “Image segmentation by semantic method,”Patt. Recog., vol. 20, no. 5, pp. 497–511, 1987.

    Google Scholar 

  10. M. Yau and S. Srihari, “A hierarchical data structure for multidimensional digital images,”Comm. ACM, vol. 26, pp. 504–515, 1983.

    Google Scholar 

  11. H. Samet and M. Tamminen, “Computing geometric properties of images represented by linear quadtrees,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-7, pp. 229–239, 1985.

    Google Scholar 

  12. C.H. Chien and J.K. Aggarwal, “A normalized quadtree representation,”Comput. Vis. Graph., Image Process., vol. 26, pp. 331–346, 1984.

    Google Scholar 

  13. C.H. Chien and J.K. Aggarwal, “Volume/surface quadtrees for the representation of three dimensional objects,”Comput. Vis. Graph., Image Process., vol. 36, pp. 100–113, 1986.

    Google Scholar 

  14. C.H. Chien, Y.B. Sim, and J.K. Aggarwal, “Generation of volume/surface octree from range data,” inProc. IEEE Conference on Computer Vision and Pattern Recognition, 1988, pp. 254–260.

  15. R.M. Haralick and L. Watson, “A facet model for image data,”Comput. Graph. Image Process., vol. 15, pp. 113–129, 1981.

    Google Scholar 

  16. N. Yokoya and M.D. Levine, “Range image segmentation based on differential geometry: A hybrid approach,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-11, pp. 643–649, 1989.

    Google Scholar 

  17. P.J. Besl and R.C. Jain, “Segmentation through variableorder surface fitting,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-10, pp. 167–192, 1988.

    Google Scholar 

  18. P.J. Besl and R. Jain, “Range image understanding,” inProc. IEEE Conference on Computer Vision and Pattern Recognition, 1985, pp. 226–233.

  19. P.J. Besl,Surfaces in Range Image Understanding, Springer-Verlag: New York, 1986.

    Google Scholar 

  20. R.C. Gonzalez and R.E. Woods,Digital Image Processing. Addison-Wesley: Reading, MA, 1992.

    Google Scholar 

  21. H. Niemann,Pattern Analysis and Understanding, Springer-Verlag: Berlin, 1981, pp. 79–92.

    Google Scholar 

  22. R.M. Haralick, “Edge and region analysis for digital image data,”Comput. Graph. Image Process., vol. 12, pp. 60–73, 1980.

    Google Scholar 

  23. R.M. Haralick, “Ridges and valleys on digital images,”Comput. Graph. Image Process., vol. 22, pp. 28–38, 1983.

    Google Scholar 

  24. D. Marr and E. Hildreth, “Theory of edge detection,”Proc. Roy. Soc. London Sec. B, vol. 207, pp. 187–217, 1980.

    Google Scholar 

  25. E.C. Hildreth, “The detection of intensity changes by computer and biological vision systems,”Compute. Vis. Graph., Image Process., vol. 22, pp. 1–27, 1983.

    Google Scholar 

  26. A. Huertas and G. Medioni, “Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-8, pp. 651–664, 1986.

    Google Scholar 

  27. S. Yakowitz and F. Szidarovszky,An Introduction to Numerical Computations, Macmillan: New York, 1989, pp. 297–322.

    Google Scholar 

  28. C.R. Brice and C.L. Fennema, “Scene analysis using regions,”Artif. Intell. vol. 1, pp. 205–226, 1970.

    Google Scholar 

  29. A.W.T. Lim, “An active vision system for 3D object recognition,” M.Eng. thesis, Nanyang Technological University, Singapore, 1991.

    Google Scholar 

  30. J. Canny, “ A computational approach to edge detection,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-8, pp. 679–798, 1986.

    Google Scholar 

  31. V. S. Nalwa and T. O. Binford, “On detecting edges,”IEEE Trans. Patt. Anal. Mach. Intell., vol. PAMI-8, pp. 699–713, 1986.

    Google Scholar 

  32. D. H. Huble and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat's visual cortex,”J. Physiol. (Lond)., vol. 160, pp. 106–154, 1962.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 2263, Nanyang Avenue, Singapore

    Alan W. T. Lim, Eam Khwang Teoh & Dinesh P. Mital

Authors
  1. Alan W. T. Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eam Khwang Teoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dinesh P. Mital
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lim, A.W.T., Teoh, E.K. & Mital, D.P. A hybrid method for range image segmentation. J Math Imaging Vis 4, 69–80 (1994). https://doi.org/10.1007/BF01250005

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01250005

Key words

Search

Navigation