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Entropic Thresholding Algorithms and their Optimizations

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Entropy Measures, Maximum Entropy Principle and Emerging Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 119))

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Abstract

Entropic thresholding algorithms are important tools in image processing. In this article, fast iterative methods are derived for the minimum cross entropy thresholding and maximum entropy thresholding algorithms using the one-point iteration scheme. Simulations performed using systhetic generated histograms and a real image show the speed advantage and the accuracy of the iterated methods.

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References

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Hong Kong Polytechnic University Hung Hom, Hong Kong, China

    C. H. Li, C. K. Lee & P. K. S. Tam

Authors
  1. C. H. Li
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  2. C. K. Lee
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  3. P. K. S. Tam
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Editor information

Editors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, 110 067, New Delhi, India

    Karmeshu

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© 2003 Springer-Verlag Berlin Heidelberg

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Li, C.H., Lee, C.K., Tam, P.K.S. (2003). Entropic Thresholding Algorithms and their Optimizations. In: Karmeshu (eds) Entropy Measures, Maximum Entropy Principle and Emerging Applications. Studies in Fuzziness and Soft Computing, vol 119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36212-8_10

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  • DOI: https://doi.org/10.1007/978-3-540-36212-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05531-7

  • Online ISBN: 978-3-540-36212-8

  • eBook Packages: Springer Book Archive

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