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Shape Preserving Sampling and Reconstruction of Grayscale Images

  • Peer Stelldinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)

Abstract

The expressiveness of a lot of image analysis algorithms depends on the question whether shape information is preserved during digitization. Most existing approaches to answer this are restricted to binary images and only consider nearest neighbor reconstruction. This paper generalizes this to grayscale images and to several reconstruction methods. It is shown that a certain class of images can be sampled with regular and even irregular grids and reconstructed with different interpolation methods without any change in the topology of the level sets of interest.

Keywords

Sampling Point Binary Image Image Function Jordan Curve Grayscale 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Peer Stelldinger
    • 1
  1. 1.Cognitive Systems GroupUniversity of HamburgHamburgGermany

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