What Can We Learn from Discrete Images about the Continuous World?

  • Ullrich Köthe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4992)


Image analysis attempts to perceive properties of the continuous real world by means of digital algorithms. Since discretization discards an infinite amount of information, it is difficult to predict if and when digital methods will produce reliable results. This paper reviews theories which establish explicit connections between the continuous and digital domains (such as Shannon’s sampling theorem and a recent geometric sampling theorem) and describes some of their consequences for image analysis. Although many problems are still open, we can already conclude that adherence to these theories leads to significantly more stable and accurate algorithms.


Point Spread Function Homotopy Type Sampling Theorem Discrete Image Noise Standard Deviation 
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 2008

Authors and Affiliations

  • Ullrich Köthe
    • 1
  1. 1.Multi-Dimensional Image Processing GroupUniversity of HeidelbergGermany

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