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Gaussian-Weighted Moving-Window Robust Automatic Threshold Selection

  • Michael H. F. Wilkinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2756)

Abstract

A multi-scale, moving-window method for local thresholding based on Robust Automatic Threshold Selection (RATS) is developed. Using a model for the noise response of the optimal edge detector in this context, the reliability of thresholds computed at different scales is determined. The threshold computed at the smallest scale at which the reliability is sufficient is used. The performance on 2-D images is evaluated on synthetic an natural images in the presence of varying background and noise. Results show the method deals better with these problems than earlier versions of RATS at most noise levels.

Keywords

Vision Graph Global Threshold Global Thresholding Edge Strength Faint Object 
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 2003

Authors and Affiliations

  • Michael H. F. Wilkinson
    • 1
  1. 1.Institute for Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

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