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Abstract

Optimal multilevel thresholding is a quite important problem in image segmentation and pattern recognition. Although efficient algorithms have been proposed recently, they do not address the issue of irregularly sampled histograms. A polynomial-time algorithm for multilevel thresholding of irregularly sampled histograms is proposed. The algorithm is polynomial not just on the number of bins of the histogram, n, but also on the number of thresholds, k, i.e. it runs in Θ(kn 2). The proposed algorithm is general enough for a wide range of thresholding and clustering criteria, and has the capability of dealing with irregularly sampled histograms. This implies important consequences on pattern recognition, since optimal clustering in the one-dimensional space can be obtained in polynomial time. Experiments on synthetic and real-life histograms show that for typical cases, the proposed algorithm can find the optimal thresholds in a fraction of a second.

Keywords

Particle Swarm Optimization Optimal Threshold Optimal Cluster Pattern Recognition Letter Algorithm Multilevel 
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

  • Luis Rueda
    • 1
  1. 1.Department of Computer ScienceUniversity of WindsorWindsorCanada

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