Advertisement

Approximation and Processing of Intensity Images with Discontinuity-Preserving Adaptive Triangular Meshes

  • Miguel Angel Garcia
  • Boris Xavier Vintimilla
  • Angel Domingo Sappa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)

Abstract

A new algorithm for approximating intensity images with adaptive triangular meshes keeping image discontinuities and avoiding optimization is presented. The algorithm consists of two main stages. In the first stage, the original image is adaptively sampled at a set of points, taking into account both image discontinuities and curvatures. In the second stage, the sampled points are triangulated by applying a constrained 2D Delaunay algorithm. The obtained triangular meshes are compact representations that model the regions and discontinuities present in the original image with many fewer points. Thus, image processing operations applied upon those meshes can perform faster than upon the original images. As an example, four simple operations (translation, rotation, scaling and deformation) have been implemented in the 3D geometric domain and compared to their image domain counterparts.1

Keywords

Original Image Gray Level Intensity Image Triangular Mesh Range Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    B. Smith and L. Rowe. “Algorithms for Manipulating Compressed Images”. IEEE Computer Graphics and Applications, vol. 13, no. 5, September 1993, 34–42.CrossRefGoogle Scholar
  2. [2]
    Shih-Fu Chang. “Compressed-domain techniques for image/video indexing and manipulation”. IEEE Int. Conf. on Image Processing, Washington DC, October 1995.Google Scholar
  3. [3]
    M. A. Garcia, A. D. Sappa and L. Basanez. “Efficient approximation of range images through data dependent adaptive triangulations”. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico, June 1997, 628–633.Google Scholar
  4. [4]
    M. A. Garcia and L. Basanez. “Fast extraction of surface primitives from range images”. IEEE Int. Conf. on Pattern Recognition,Vienna, 1996, 568–572.Google Scholar
  5. [5]
    M. A. Garcia, Boris X. Vintimilla and A. D. Sappa. “Efficient approximation of gray-scale images through bounded error triangular meshes”. IEEE Int. Conf. on Image Processing, Kobe, Japan, October 1999.Google Scholar
  6. [6]
    Leila De Floriani. “A pyramidal data structure for triangle-based surface description”. IEEE Computer Graphics & Applications, 9(2), March 1989, 67–78Google Scholar
  7. [7]
    R. C. Wilson and E. R. Hancock. “A minimum-variance adaptive surface mesh”. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico, June 1997, 634–639.Google Scholar
  8. [8]
    D. Terzopoulos and M. Vasilescu. “Sampling and reconstruction with adaptive meshes”. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Hawaii, USA, June 1991, 70–75.Google Scholar
  9. [9]
    M. Vasilescu and D. Terzopoulos. “Adaptive meshes and shells: irregular triangulation, discontinuities and hierarchical subdivision”. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Champaign, USA, June 1992, 829–832.Google Scholar
  10. [10]
    T. Gevers and V. K. Kajcovski. “Image segmentation by directed region subdivision”. IEEE Int. Conf. on Pattern Recognition, Jerusalen, Israel, Octuber 1994, 342–346.Google Scholar
  11. [11]
    J. Canny. “A computational approach to edge detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, November 1986, 679–698.CrossRefGoogle Scholar
  12. [12]
    J. R. Shewchuk. “Triangle: engineering a 2D quality mesh generator and Delaunay triangulator”. First Workshop on Applied Computational Geometry, Philadelphia, Pennsylvania, ACM, May 1996, 124–133.Google Scholar
  13. [13]
    A. Ciampalini, P. Cignoni, C. Montani and R. Scopigno. “Multiresolution decimation based on global error”. The Visual Computer, Springer-Verlag, 13(5), June 1997.Google Scholar
  14. [14]
    S. E. Umbaugh. Computer Vision and Image Processing. Prentice-Hall International Editions, 1998.Google Scholar
  15. [15]
    D. Molloy and P. F. Whelan. “Active-mesh self-initialisation”. Irish Machine Vision and Image Processing Conference. P. Whelan (Ed.), ISBN 1 872 327 222, Dublin, Ireland, September 1999, 116–130.Google Scholar
  16. [16]
    T. Gervers and A. W. Smeulders. “Combining region splitting and edge detection through guided Delaunay image subdivision”. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico, June 1997, 1021–1026.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Miguel Angel Garcia
    • 1
  • Boris Xavier Vintimilla
    • 2
  • Angel Domingo Sappa
    • 3
  1. 1.Department of Computer Science and MathematicsRovira i Virgili UniversityTarragonaSpain
  2. 2.Institute of Organization and Control of Industrial SystemsPolytechnic University of CataloniaBarcelonaSpain
  3. 3.LAAS - CNRSOffice B157Toulouse, Cedex 4France

Personalised recommendations