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Hierarchical Segmentation of Sparse Surface Data Using Energy-Minimization Approach

  • Raid Al-Tahir

Abstract

The main objective for this research is to develop an algorithm that produces a dense representation of a surface from a sparse set of observations and facilitates preliminary labeling of discontinuities in the surface. The solution to these issues is of a great interest to the new trends and applications in digital photogrammetry, particularly for large-scale urban imagery.

This study adopts the approach of a concurrent interpolation of the surface and detection of its discontinuities by the weak membrane. The solution was achieved through developing a multigrid implementation of the Graduate Non-Convexity (GNC) algorithm. The conducted experiments proved that the developed method is adequate and applicable for dealing with large-scale images of urban areas as it was successful in producing a realistic surface representation and fulfilling other set criteria.

Key words

Surface Reconstruction Discontinuioty Detection Multigrid Regularization 

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References

  1. [1]
    Mikhail E., Bethel J. and McGlone C. 2001. Modern Photogrammetry, John Wiley, USA.Google Scholar
  2. [2]
    Keating T., Garland P. and Dörstel C. 2003. ‘Photogrammetry Goes Digital,. GIS Development.Google Scholar
  3. [3]
    Schenk T. 1999. Digital Photogrammetry, Volume 1, TerraScience, OH, USA.Google Scholar
  4. [4]
    Al-Tahir R. 1996. Interpolation and Analysis in Hierarchical Surface Reconstruction, Report 435, Department of Geodetic Science and Surveying, The Ohio State University, OH.Google Scholar
  5. [5]
    Al-Tahir R. 2003. ‘Segmentation of Lidar surface data using energy-minimization approach'. Conradi Research Review 2(2), pp. 76–85.Google Scholar
  6. [6]
    Hewer G., Kenney C. and Manjunath B.S. 1998. ‘Variational image segmentation using boundary functions'. IEEE Transactions on Image Processing 7(9), pp. 1269–1282.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    Watson D. 1992. Contouring: A Guide to the Analysis and Display of Spatial Data. Pergamon Press.Google Scholar
  8. [8]
    Wolberg G. 1990. Digital Image Warping, IEEE Computer Society Press, CA.Google Scholar
  9. [9]
    Geman S. and Geman D. 1984. ‘Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images'. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), pp. 721–74.CrossRefzbMATHGoogle Scholar
  10. [10]
    Blake A. and Zisserman A. 1987. Visual Reconstruction, MIT Press, MA.Google Scholar
  11. [11]
    Nielsen M. 1997. ‘Graduated Nonconvexity by Functional Focusing'. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), pp. 521–525.CrossRefGoogle Scholar
  12. [12]
    Nakamura S. 1991. Applied Numerical Methods with Software, Prentice Hall, NJ.Google Scholar
  13. [13]
    Briggs W., Henson V. and McCormick S. 2000. A Multigrid Tutorial, 2nd edition, Society for Industrial & Applied Mathematics, Pennsylvania.zbMATHGoogle Scholar
  14. [14]
    Schenk T. and Csathó B. 2002. ‘Fusion of Lidar data and aerial imagery for a more complete surface description'. Proceedings of ISPRS Symposium Photogrammetric Computer Vision (Austria) IAPRS 34(3A), pp 310–317.Google Scholar
  15. [15]
    Witkin A., Terzopoulos D. and Kass M. 1987. ‘Signal matching through scale space', International Journal of Computer Vision 1, pp. 133–144.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Raid Al-Tahir
    • 1
  1. 1.Centre for Caribbean Land and Environmental Appraisal Research (CLEAR) Department of Surveying and Land InformationThe University of the West IndiesTrinidad and Tobago

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